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AnalysisHarmonic Analysis·Captain: wurtle

A uniform Hilbert transform estimate for Lipschitz directionsResearch Paper

Motivation

The Hilbert transform Hf(x)=p.v.∫f(x−t) dttHf(x)=\mathrm{p.v.}\int f(x-t)\,\frac{dt}{t}Hf(x)=p.v.∫f(x−t)tdt​ is the basic singular integral of harmonic analysis. In the plane one can take it along a line through each point, with the direction of the line allowed to depend on the point: given a unit vector field v:R2→S1v:\mathbb R^2\to S^1v:R2→S1, integrate fff along the segment through xxx in direction v(x)v(x)v(x). Whether such directional Hilbert transforms are bounded on L2L^2L2 is one of the central problems connecting singular integrals, Kakeya-type geometry and time–frequency analysis. If vvv depends on only one coordinate, the L2L^2L2 problem already contains Carleson's theorem on almost-everywhere convergence of Fourier series. For general fields some regularity of vvv is necessary, and some truncation of the integration length is necessary because a field can turn.

Stein asked whether a Lipschitz field suffices when the integration length is at most a small multiple of 1/Lip(v)1/\mathrm{Lip}(v)1/Lip(v). In the formulation of Lacey and Li, this is a uniform weak-(2,2)(2,2)(2,2) conjecture. A parallel conjecture of Zygmund concerns the corresponding maximal averages.

Timeline

  • 1966. Carleson proves almost-everywhere convergence of Fourier series of L2L^2L2 functions (doi:10.1007/BF02392815); the one-variable-field L2L^2L2 case of the directional problem reduces to this.
  • 1987. Stein lists the Lipschitz directional Hilbert transform among problems in harmonic analysis in his ICM address (Problems in harmonic analysis related to curvature and oscillatory integrals).
  • 1989. Bourgain proves an L2L^2L2 bound for the maximal averaging operator along analytic vector fields (doi:10.1017/CBO9780511662294.006).
  • 2006. Lacey and Li prove annular weak-(2,2)(2,2)(2,2) and strong LpL^pLp, p>2p>2p>2, bounds for directional Hilbert transforms with measurable directions (doi:10.1090/S0002-9947-06-03869-4), and a Lipschitz Kakeya maximal theorem.
  • 2010. Lacey and Li's memoir develops the relation between maximal operators and the Hilbert-transform problem, with conditional results requiring stronger maximal estimates or C1+ηC^{1+\eta}C1+η fields (doi:10.1090/S0065-9266-10-00572-7).
  • 2012. Stein and Street prove local LpL^pLp bounds for singular Radon transforms associated with real-analytic maps (doi:10.1016/j.aim.2011.11.016).
  • 2013. Bateman proves single-annulus LpL^pLp estimates for fields depending on one coordinate (doi:10.4171/RMI/748); Bateman and Thiele prove strong LpL^pLp bounds, 3/2<p<∞3/2<p<\infty3/2<p<∞, for the full transform along one-variable fields (doi:10.2140/apde.2013.6.1577).
  • 2015–2017. Guo extends the one-variable setting to fields constant along Lipschitz curves (doi:10.2140/apde.2015.8.1263, doi:10.1090/tran/6750) and proves single-annulus variation-norm bounds for Lipschitz fields; Guo and Thiele treat a lacunary model of Lipschitz fields (doi:10.1112/S0025579316000280).
  • 2018. Di Plinio and Parissis extend the lacunary framework (doi:10.1007/s11856-018-1724-y); Di Plinio, Guo, Thiele and Zorin-Kranich show that uniform L2L^2L2 bounds on vertical frequency bands imply the full short principal-value bound under a small Lipschitz condition (doi:10.1016/j.jfa.2018.07.005).

The source of this mission, an OpenAI preprint dated September 25, 2026, claims the uniform strong L2L^2L2 bound at short scale for every Lipschitz field.

Setting

Let v:R2→S1v:\mathbb R^2\to S^1v:R2→S1 be Lipschitz with Lip(v)≤1\mathrm{Lip}(v)\le1Lip(v)≤1. For 0<ε<a0<\varepsilon<a0<ε<a the short directional Hilbert transform is

Hv,aεf(x)=∫ε<∣t∣<af(x−t v(x)) dtt.H^{\varepsilon}_{v,a}f(x)=\int_{\varepsilon<|t|<a} f\bigl(x-t\,v(x)\bigr)\,\frac{dt}{t}.Hv,aε​f(x)=∫ε<∣t∣<a​f(x−tv(x))tdt​.

The direction is frozen at the output point xxx. For a Schwartz function fff the principal value Hv,af=lim⁡ε↓0Hv,aεfH_{v,a}f=\lim_{\varepsilon\downarrow0}H^\varepsilon_{v,a}fHv,a​f=limε↓0​Hv,aε​f exists at every point, since f(x−tv(x))−f(x+tv(x))=Ox(∣t∣)f(x-tv(x))-f(x+tv(x))=O_x(|t|)f(x−tv(x))−f(x+tv(x))=Ox​(∣t∣).

Formalization targets

Goal: Theorem 1.1

There are absolute constants a∗∈(0,1/2)a_*\in(0,1/2)a∗​∈(0,1/2) and C∗<∞C_*<\inftyC∗​<∞ such that every unit field vvv with Lip(v)≤1\mathrm{Lip}(v)\le1Lip(v)≤1 satisfies, for all f∈S(R2)f\in\mathcal S(\mathbb R^2)f∈S(R2),

sup⁡0<ε<a∗∥Hv,a∗εf∥L2≤C∗∥f∥L2,∥Hv,a∗f∥2≤C∗∥f∥2,\sup_{0<\varepsilon<a_*}\|H^{\varepsilon}_{v,a_*}f\|_{L^2}\le C_*\|f\|_{L^2},\qquad \|H_{v,a_*}f\|_2\le C_*\|f\|_2,0<ε<a∗​sup​∥Hv,a∗​ε​f∥L2​≤C∗​∥f∥L2​,∥Hv,a∗​​f∥2​≤C∗​∥f∥2​, ∣{x:∣Hv,a∗f(x)∣>λ}∣≤C∗2λ−2∥f∥22(λ>0),\bigl|\{x:|H_{v,a_*}f(x)|>\lambda\}\bigr|\le C_*^2\lambda^{-2}\|f\|_2^2\qquad(\lambda>0),​{x:∣Hv,a∗​​f(x)∣>λ}​≤C∗2​λ−2∥f∥22​(λ>0),

and the truncations and the principal value extend to bounded operators on L2L^2L2 with norm at most C∗C_*C∗​. The constants a∗a_*a∗​ and C∗C_*C∗​ are existential; no numerical value is fixed. The Lean statement OAI.LipschitzHilbert.main is open on the platform.

The proposal also contains OAI.LipschitzHilbert.uniform_truncation, the rescaled form for KKK-Lipschitz fields at every outer length R≤1/(106K)R\le 1/(10^6K)R≤1/(106K). By the dilation x↦Kxx\mapsto Kxx↦Kx it is the same estimate at the reciprocal-Lipschitz scale; its explicit constant 10−610^{-6}10−6 is a convention of the formalization, not of the paper.

Significance

The theorem answers the short-scale form of Stein's question affirmatively and with a strong (not only weak-type) L2L^2L2 bound, for fields depending on both coordinates and with no regularity beyond Lipschitz. Earlier unconditional results required the field to depend on one variable, to be constant along a foliation, to be analytic, to take lacunary directions, or restricted the frequency to one annulus. The uniformity in the inner truncation is an operator bound for each truncation, which is what the principal value and the weak-type bound are derived from.

The result is claimed in an OpenAI preprint of 87 pages; it has not been peer reviewed and no machine-checked proof exists. The Lean goal records the complete short-scale conclusion; a formal proof would certify a long chain of time–frequency arguments (wave-packet decompositions, size and density selection, a Lipschitz Kakeya input, and a phase-gain iteration).

Difficulty

Restricting to short scales limits how far the field turns but does not make the operator a Fourier multiplier: the line of integration still varies with the output point. Positive (maximal) operator bounds lose all oscillation, while the one-variable reduction to Carleson's theorem is unavailable when vvv depends on both coordinates. The known band-to-full reassembly reduces matters to one vertical frequency band, but on a band one needs a restricted estimate with a logarithmic gain in the ratio of the measures of the input and test sets; the Lacey–Li Kakeya estimate only provides a polynomial loss in that ratio, so an additional oscillatory saving is required.

Formalization scope

  • The plane is EuclideanSpace ℝ (Fin 2); inputs are SchwartzMap Plane ℂ, norms are eLpNorm _ 2 volume in ℝ≥0∞.
  • The field is v : Plane → Plane with LipschitzWith 1 v and ‖v x‖ = 1 for all x.
  • twoSidedTruncation v ε a f x is the sum of interval integrals over [−a,−ε][-a,-\varepsilon][−a,−ε] and [ε,a][\varepsilon,a][ε,a] of t−1f(x−tv(x))t^{-1}f(x-tv(x))t−1f(x−tv(x)); smoothPrincipalValue integrates the symmetric quotient over (0,a)(0,a)(0,a), with the removable value at t=0t=0t=0 filled by the derivative.
  • ShortHilbertConclusion a C bundles the five clauses (truncation bound for 0<ε<a0<\varepsilon<a0<ε<a, principal-value bound, weak-(2,2) bound at every finite nonzero level, and bounded Lp extensions agreeing a.e. on Schwartz inputs). The goal asks for a∈(0,1/2)a\in(0,1/2)a∈(0,1/2) and C>0C>0C>0.

A complete development needs singular-integral and Littlewood–Paley machinery on R2\mathbb R^2R2, wave-packet analysis and a Lipschitz Kakeya maximal estimate; much of this is reusable for other time–frequency problems. Contributions formalizing the band-restricted estimate, the band-to-full reassembly, or the one-dimensional inputs (the maximal truncated Hilbert transform) are welcome.

Selected references

  • OpenAI, A uniform Hilbert transform estimate for Lipschitz directions, preprint, September 25, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-uniform-Hilbert-transform-estimate-for-Lipschitz-directions-September-25-2026/main.pdf
  • M. Lacey, X. Li, On a conjecture of E. M. Stein on the Hilbert transform on vector fields, Mem. Amer. Math. Soc., 2010. https://doi.org/10.1090/S0065-9266-10-00572-7
  • M. T. Lacey, X. Li, Maximal theorems for the directional Hilbert transform on the plane, Trans. Amer. Math. Soc., 2006. https://doi.org/10.1090/S0002-9947-06-03869-4
  • M. Bateman, C. Thiele, LpL^pLp estimates for the Hilbert transforms along a one-variable vector field, Anal. PDE, 2013. https://doi.org/10.2140/apde.2013.6.1577
  • F. Di Plinio, S. Guo, C. Thiele, P. Zorin-Kranich, Square functions for bi-Lipschitz maps and directional operators, J. Funct. Anal., 2018. https://doi.org/10.1016/j.jfa.2018.07.005
  • S. Guo, C. Thiele, Hilbert transforms along Lipschitz direction fields: a lacunary model, Mathematika, 2017. https://doi.org/10.1112/S0025579316000280
  • E. M. Stein, B. Street, Multi-parameter singular Radon transforms III: real analytic surfaces, Adv. Math., 2012. https://doi.org/10.1016/j.aim.2011.11.016
  • L. Carleson, On convergence and growth of partial sums of Fourier series, Acta Math., 1966. https://doi.org/10.1007/BF02392815
3 thms1 active userReviewed
AnalysisGeometry & Topology·Captain: wurtle

Koebe's Circle-Domain ConjectureResearch Paper

Motivation

The Riemann mapping theorem says that every simply connected proper domain in the plane is conformally equivalent to the unit disk. For domains with more complicated complements one needs a model with many "holes". Koebe's Kreisnormierungsproblem (1908) asks whether every domain in the Riemann sphere is conformally equivalent to a circle domain, one whose complementary components are all round disks or points. Such a model would give a canonical geometric picture of an arbitrary planar domain, and it is closely tied to circle packings, Kleinian groups and the geometry of hyperbolic surfaces of genus zero.

Timeline

  • 1908. Koebe poses the problem (Nachr. Ges. Wiss. Göttingen, 1908).
  • 1920. Koebe's treatment of the finitely connected case (doi:10.1007/BF01199400).
  • 1993. He and Schramm prove the conjecture for countably connected domains (doi:10.2307/2946541).
  • 1995. Schramm introduces transboundary extremal length and treats domains bounded by points and KKK-quasicircles (doi:10.1007/BF02788827).
  • 2025. Rajala shows that arbitrary interior exhaustions can fail to have circle-domain limits and proves an exhaustion refinement theorem for countably connected domains (doi:10.1353/ajm.2025.a966291); Ntalampekos and Rajala study exhaustions of circle domains (arXiv:2312.06840).
  • 2026. Esmayli and Rajala prove uniformization for cospread domains and under a quasitripod condition (arXiv:2401.08485); Karafyllia and Ntalampekos treat spherical Gromov-hyperbolic domains (arXiv:2405.13782). Ntalampekos surveys the area (arXiv:2603.15098).

The source of this mission, an OpenAI preprint dated September 23, 2026, claims the conjecture in its unrestricted form.

Setting

The Riemann sphere is C^=C∪{∞}\widehat{\mathbb C}=\mathbb C\cup\{\infty\}C=C∪{∞}, with local coordinate zzz near finite points and 1/z1/z1/z near ∞\infty∞. A domain is a nonempty connected open subset. A map fff is conformal at ppp if it is continuous at ppp and, in these coordinates, complex differentiable at ppp with nonzero derivative. A conformal equivalence f:U→Vf:U\to Vf:U→V is a bijection that is conformal at every point of UUU and whose inverse is conformal at every point of VVV.

A closed round disk in C^\widehat{\mathbb C}C is the image of the closed unit disk under a Möbius transformation z↦(az+b)/(cz+d)z\mapsto (az+b)/(cz+d)z↦(az+b)/(cz+d), ad−bc≠0ad-bc\ne0ad−bc=0; these are closed Euclidean disks, closed half-planes together with ∞\infty∞, and complements of open disks. A circle domain is a domain each of whose complementary connected components is a closed round disk or a single point. The complement may be empty, finite, countable or uncountable.

Formalization targets

Goal: Theorem 1.1 (circle-domain uniformization)

∀ G⊂C^ domain∃ Ω circle domain, ∃ f:G→ conformal Ω bijective.\forall\,G\subset\widehat{\mathbb C}\ \text{domain}\quad\exists\,\Omega\ \text{circle domain},\ \exists\,f:G\xrightarrow{\ \text{conformal}\ }\Omega\ \text{bijective}.∀G⊂C domain∃Ω circle domain, ∃f:G conformal ​Ω bijective.

Lean: OAI.Problem047.koebe_circle_domain, open on the platform.

Significance

The theorem would complete the existence half of Koebe's problem with no restriction on the number, size or geometry of the complementary components. The paper derives a consequence for hyperbolic geometry (Corollary 8.1): every complete hyperbolic surface of genus zero is isometric to the boundary of the convex hull of a closed set in ∂∞H3\partial_\infty\mathbb H^3∂∞​H3 whose components are round disks or points. Combined with the exhaustion theorem of Ntalampekos and Rajala, it also gives convergent finitely connected approximations for every proper domain. Uniqueness up to Möbius maps is a different question (the He–Schramm rigidity conjecture), addressed in the companion mission on removable boundaries.

The result is claimed in an OpenAI preprint that has not been peer reviewed; no machine-checked proof exists. Mathlib has neither the Riemann mapping theorem in full generality nor finitely connected circle-domain uniformization, so a formal proof would build substantial reusable complex-analytic infrastructure.

Difficulty

The standard approach uniformizes finitely connected approximations, whose complements are finitely many round disks, and passes to a limit. The difficulty is control at the limit: with possibly uncountably many complementary components, a limiting component can be strictly larger than the round disk obtained by following one approximating disk, or can be a non-round continuum for which no disk was followed at all. Rajala showed that arbitrary exhaustions can indeed fail to produce circle-domain limits, so the approximations must be chosen with care, and all components must be controlled simultaneously, not one at a time.

Formalization scope

  • Sphere := OnePoint ℂ; Möbius maps are Matrix.GeneralLinearGroup (Fin 2) ℂ acting on it.
  • IsConformalAt f p: ContinuousAt f p and a nonzero HasDerivAt of fff in the charts chart p, chart (f p) (identity coordinate at finite points, z↦1/zz\mapsto1/zz↦1/z at ∞\infty∞).
  • IsConformalEquivalence f U V: fff conformal on UUU, MapsTo f U V, and a conformal g on VVV with MapsTo g V U that is a two-sided inverse.
  • IsCircleDomain V: open, IsConnected (hence nonempty), and every connectedComponentIn Vᶜ p is a singleton or IsRoundClosedDisk (a Möbius image of the closed unit disk).
  • The goal quantifies over all open connected U; there is no countability or regularity assumption on the complement.

A complete development needs finitely connected uniformization, normal families on the sphere, Dirichlet energy and extremal length, and component correspondence for limits. Contributions formalizing the named intermediate results (Theorem 3.1 finite transfer, Theorem 4.3 compatible barrier lemma, Theorem 5.1 positive-law alternative, Theorem 7.5 retained-test transfer) or the classical He–Schramm countable case are welcome.

Selected references

  • OpenAI, Koebe's Circle-Domain Conjecture, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Koebes-Circle-Domain-Conjecture-September-23-2026/paper.pdf
  • P. Koebe, Über die Uniformisierung beliebiger analytischer Kurven (Dritte Mitteilung), Nachr. Ges. Wiss. Göttingen, 1908.
  • P. Koebe, Abhandlungen zur Theorie der konformen Abbildung VI, Math. Z., 1920. https://doi.org/10.1007/BF01199400
  • Z.-X. He, O. Schramm, Fixed points, Koebe uniformization and circle packings, Ann. of Math., 1993. https://doi.org/10.2307/2946541
  • O. Schramm, Transboundary extremal length, J. Analyse Math., 1995. https://doi.org/10.1007/BF02788827
  • K. Rajala, Uniformization of planar domains by exhaustion, Amer. J. Math., 2025. https://doi.org/10.1353/ajm.2025.a966291
  • D. Ntalampekos, K. Rajala, Exhaustions of circle domains, IMRN, 2025. https://doi.org/10.1093/imrn/rnaf296
  • B. Esmayli, K. Rajala, Conformal uniformization of domains bounded by quasitripods, Duke Math. J., 2026. https://doi.org/10.1215/00127094-2025-0035
  • C. Karafyllia, D. Ntalampekos, Uniformization of Gromov hyperbolic domains by circle domains, Duke Math. J., 2026. https://doi.org/10.1215/00127094-2025-0076
  • D. Ntalampekos, Uniformization problems in the plane: A survey, preprint, 2026. https://arxiv.org/abs/2603.15098
2 thms1 active userReviewed
AnalysisGeometry & Topology·Captain: wurtle

Removable Boundaries and Rigidity of Circle DomainsResearch Paper

Motivation

A circle domain is a domain in the Riemann sphere whose complementary components are round disks or points. Koebe asked in 1908 whether every domain is conformally equivalent to a circle domain (existence). A second question is rigidity: when is that circle-domain model unique up to Möbius transformations? For finitely and countably connected domains the model is unique, but for domains with uncountably many boundary components uniqueness can fail, and the boundary geometry decides. He and Schramm conjectured that a circle domain is rigid exactly when its boundary is conformally removable. Rigidity is what makes the circle-domain model canonical, and it connects conformal geometry with removability questions for quasiconformal and Sobolev maps.

Timeline

  • 1993. He and Schramm prove uniformization and rigidity for countably connected circle domains (doi:10.2307/2946541).
  • 1994. He and Schramm prove rigidity when the boundary has σ\sigmaσ-finite linear measure, and conjecture that rigidity is equivalent to conformal removability of the boundary (doi:10.1007/BF01231761).
  • 2000. Jones and Smirnov relate removability for continuous Sobolev functions to quasiconformal removability (doi:10.1007/BF02384320).
  • 2016. Younsi proves that conformal rigidity is equivalent to quasiconformal rigidity and studies the removability formulations (doi:10.1016/j.aim.2016.08.039).
  • 2020. Ntalampekos and Younsi prove rigidity under square integrability of the quasihyperbolic distance, covering Hölder and John circle domains (doi:10.1007/s00222-019-00921-1).
  • 2023–2024. Ntalampekos proves rigidity when point components are countably negligible for extremal distance (CNED) and continuous Sobolev extension across closed CNED sets (doi:10.1090/tran/8923, doi:10.1007/s00029-024-00951-5).
  • 2025. Rajala constructs a rigid circle domain with non-removable boundary, so rigidity does not imply removability (doi:10.1112/plms.70081).

The source of this mission, an OpenAI preprint dated September 23, 2026, claims the remaining implication: removability implies rigidity.

Setting

The Riemann sphere C^=C∪{∞}\widehat{\mathbb C}=\mathbb C\cup\{\infty\}C=C∪{∞} has local coordinates zzz and 1/z1/z1/z. A map is conformal at ppp if it is continuous there and complex differentiable with nonzero derivative in these coordinates; a conformal equivalence f:Ω→Ω′f:\Omega\to\Omega'f:Ω→Ω′ is a bijection, conformal on Ω\OmegaΩ, with conformal inverse. A Möbius transformation is z↦(az+b)/(cz+d)z\mapsto(az+b)/(cz+d)z↦(az+b)/(cz+d) with ad−bc≠0ad-bc\neq0ad−bc=0. A closed round disk is the Möbius image of the closed unit disk. A circle domain is a nonempty connected open set whose complementary components are closed round disks or points.

A compact E⊂C^E\subset\widehat{\mathbb C}E⊂C is conformally removable if every orientation-preserving homeomorphism of C^\widehat{\mathbb C}C that is conformal on C^∖E\widehat{\mathbb C}\setminus EC∖E is Möbius. A circle domain Ω\OmegaΩ is conformally rigid if every conformal equivalence from Ω\OmegaΩ onto another circle domain is the restriction of a Möbius transformation.

Formalization targets

Goal: Theorem 1.1

Ω,Ω′ circle domains,  ∂Ω conformally removable,  f:Ω→Ω′ conformal equivalence ⟹ ∃M Mo¨bius: f=M∣Ω.\Omega,\Omega'\ \text{circle domains},\ \ \partial\Omega\ \text{conformally removable},\ \ f:\Omega\to\Omega'\ \text{conformal equivalence}\ \Longrightarrow\ \exists M\ \text{Möbius}:\ f=M|_\Omega.Ω,Ω′ circle domains,  ∂Ω conformally removable,  f:Ω→Ω′ conformal equivalence ⟹ ∃M Mo¨bius: f=M∣Ω​.

Lean: OAI.Problem047.removability_implies_rigidity, open on the platform. The complement may have any cardinality, and no boundary extension of fff is assumed.

Significance

Together with Rajala's counterexample to the converse, the theorem would settle the He–Schramm rigidity conjecture: removability is sufficient but not necessary. It subsumes the earlier sufficient conditions (σ\sigmaσ-finite length, Hölder/John domains, CNED point sets) whenever those boundaries are removable. The intermediate Theorem 5.1, a continuous Sobolev extension theorem across compact totally disconnected conformally removable sets, is of independent interest. The result is claimed in an OpenAI preprint that has not been peer reviewed, and no machine-checked proof exists.

Difficulty

Removability is a statement about homeomorphisms of the whole sphere, while rigidity starts from a map defined only on the domain. The obvious route, extending fff to a homeomorphism of the sphere and then invoking removability, fails: a point component of the complement of Ω\OmegaΩ may correspond to a disk component of Ω′\Omega'Ω′ and vice versa, so the coordinates of fff need not extend continuously, and nothing a priori controls boundary behaviour at uncountably many point components whose union can have positive area.

Formalization scope

  • Sphere := OnePoint ℂ, Möbius maps are Matrix.GeneralLinearGroup (Fin 2) ℂ acting on it; conformality uses the charts zzz and 1/z1/z1/z with HasDerivAt and a nonzero derivative.
  • IsCircleDomain: open, IsConnected, and every connectedComponentIn of the complement is a singleton or a Möbius image of the closed unit disk.
  • IsConformallyRemovable E: IsCompact E and every homeomorphism h : Sphere ≃ₜ Sphere that is homotopic to the identity (orientation preserving) and conformal on Eᶜ equals some Möbius map everywhere.
  • The boundary is frontier U; the conclusion is ∃ M, ∀ p ∈ U, f p = M • p.
  • The shared definitions are those of the Koebe circle-domain mission of the same family.

A complete development needs the theory of conformal maps on the sphere, Sobolev functions and Dirichlet energy in the plane, the measurable Riemann mapping theorem (used for the conductivity deformation), and component correspondence. Contributions formalizing Theorem 5.1 (continuous Sobolev extension), Proposition 6.2 (common point traces), or the He–Schramm countable case are welcome.

Selected references

  • OpenAI, Removable Boundaries and Rigidity of Circle Domains, preprint, September 23, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Removable-Boundaries-and-Rigidity-of-Circle-Domains-September-23-2026/paper.pdf
  • Z.-X. He, O. Schramm, Fixed points, Koebe uniformization and circle packings, Ann. of Math., 1993. https://doi.org/10.2307/2946541
  • Z.-X. He, O. Schramm, Rigidity of circle domains whose boundary has σ-finite linear measure, Invent. Math., 1994. https://doi.org/10.1007/BF01231761
  • M. Younsi, Removability, rigidity of circle domains and Koebe's conjecture, Adv. Math., 2016. https://doi.org/10.1016/j.aim.2016.08.039
  • D. Ntalampekos, M. Younsi, Rigidity theorems for circle domains, Invent. Math., 2020. https://doi.org/10.1007/s00222-019-00921-1
  • D. Ntalampekos, Rigidity and continuous extension for conformal maps of circle domains, Trans. Amer. Math. Soc., 2023. https://doi.org/10.1090/tran/8923
  • D. Ntalampekos, CNED sets: countably negligible for extremal distances, Selecta Math., 2024. https://doi.org/10.1007/s00029-024-00951-5
  • P. W. Jones, S. K. Smirnov, Removability theorems for Sobolev functions and quasiconformal maps, Ark. Mat., 2000. https://doi.org/10.1007/BF02384320
  • K. Rajala, Rigid circle domains with non-removable boundaries, Proc. Lond. Math. Soc., 2025. https://doi.org/10.1112/plms.70081
  • OpenAI, Koebe's Circle-Domain Conjecture, preprint, September 23, 2026.
2 thms1 active userReviewed
Algebraic GeometryAnalysisDifferential Geometry·Captain: wurtle

Symmetry of semialgebraic bounded domains with compact quotientResearch Paper

Motivation: which bounded domains cover compact spaces?

A bounded symmetric domain is a bounded connected open set in Cm\mathbb C^mCm with, at every point, a holomorphic involution having that point as an isolated fixed point. Examples are the ball, the polydisc and the Siegel upper half-spaces in their bounded realizations. They are the universal covers of compact locally symmetric varieties, such as compact quotients of the ball, and play a central role in algebraic geometry and number theory. A classical theme in several complex variables is that a bounded domain with a large automorphism group must be very special: Wong and Rosay showed that an automorphism orbit accumulating at a strongly pseudoconvex boundary point forces the ball, and Frankel showed that convex domains with compact quotients are symmetric.

Kollár and Pardon, studying algebraic varieties whose universal cover is semialgebraic, asked the following bounded-domain question: if a bounded semialgebraic open subset of a complex affine variety admits a properly discontinuous cocompact group of biholomorphisms, must it be a bounded symmetric domain? Semialgebraicity is a finiteness condition on the shape of the domain. It does not make the boundary smooth or convex, and it imposes nothing on the group.

Timeline

  • 1970 — Vey proves that a divisible generalized Siegel domain is symmetric (Ann. Sci. ÉNS 3).
  • 1977 — Wong characterizes the ball by its automorphism group among strongly pseudoconvex domains (Invent. Math. 41).
  • 1979 — Rosay localizes Wong's theorem to a single C2C^2C2 strongly pseudoconvex boundary point (Ann. Inst. Fourier 29).
  • 1989 — Frankel proves that a convex hyperbolic domain with compact quotient is a bounded symmetric domain, including non-free actions (Acta Math. 163).
  • 2012 — Kollár and Pardon pose the bounded semialgebraic domain question (arXiv v2, Question 25) in Algebraic varieties with semialgebraic universal cover (J. Topol. 5; arXiv:1104.2309v2).
  • 2021 — Zimmer shows that a C1,1C^{1,1}C1,1-bounded domain covering a compact manifold is a ball (Indiana Univ. Math. J. 70).
  • 2026 — An OpenAI preprint, Symmetry of semialgebraic bounded domains with compact quotient (OpenAI Math Release, September 24, 2026), claims an affirmative answer to the Kollár–Pardon question. It has not been peer reviewed, and its proof is not formally verified.

Setting

An affine variety is a reduced complex algebraic set V⊂CnV\subset\mathbb C^nV⊂Cn, the common zero locus of a set of polynomials. A subset of Cn\mathbb C^nCn is semialgebraic if it is a finite Boolean combination of sets {p=0}\{p=0\}{p=0} and {p>0}\{p>0\}{p>0}, with ppp a real polynomial in the real and imaginary parts of the coordinates. Let U⊆VU\subseteq VU⊆V be open in VVV, connected, semialgebraic and bounded. A map on UUU is holomorphic if near each point it is the restriction of a holomorphic map on an open subset of Cn\mathbb C^nCn; a biholomorphism is a homeomorphism that is holomorphic in both directions. A group Γ\GammaΓ acts properly discontinuously if the action is proper for the discrete topology on Γ\GammaΓ (finite stabilizers are allowed), and the action is cocompact if U/ΓU/\GammaU/Γ is compact. UUU is smooth if each point has a neighbourhood in UUU biholomorphic to an open subset of some Cm\mathbb C^mCm.

Formalization targets

Goal: semialgebraic bounded domains with compact quotient are symmetric (Theorem 1.1)

Let UUU be a nonempty connected semialgebraic bounded open subset of a complex affine variety V⊂CnV\subset\mathbb C^nV⊂Cn, and let a group Γ\GammaΓ act on UUU by biholomorphisms, properly discontinuously and with U/ΓU/\GammaU/Γ compact. Then

U is smooth, andU≅D biholomorphically for some bounded symmetric domain D⊂Cm.U\ \text{is smooth, and}\quad U\cong D\ \text{biholomorphically for some bounded symmetric domain } D\subset\mathbb C^m .U is smooth, andU≅D biholomorphically for some bounded symmetric domain D⊂Cm.

The goal statement is published on the platform with status Open.

Significance

The result itself. The theorem answers the Kollár–Pardon question affirmatively. The ambient variety may be singular and nonnormal, the quotient may have finite quotient singularities, and no convexity, boundary regularity or homogeneity is assumed. It shows that semialgebraicity of a single affine realization forces the classical picture: a compact quotient of a semialgebraic bounded domain is a compact quotient of a bounded symmetric domain. This complements the universal-cover classification of the companion preprint on semialgebraic universal covers of normal projective varieties.

Formalizing it. The statement only uses polynomials, real-analytic Boolean combinations, holomorphic maps on subsets of Cn\mathbb C^nCn and group actions, all available in Mathlib. The proof, however, draws on Nash cell decompositions, analytic discs, scaling limits of automorphisms and Vey's theorem on divisible Siegel domains, none of which is formalized. A formal development would provide reusable semialgebraic geometry and several-complex-variables infrastructure.

Difficulty

The classical rigidity arguments (Wong–Rosay, Frankel, Zimmer) need a smooth strongly pseudoconvex or convex boundary point at which rescaled automorphisms converge to a model domain. A semialgebraic boundary can have corners with several complex-normal directions, and the normal fibres can shrink at rates that depend on tangential parameters, so the naive rescaling limit can collapse to a degenerate set. Moreover UUU is not known to be a manifold at the start, so smoothness must be proved from the group action before any differential geometry is available. The heart of the proof is a noncollapse estimate that yields a Siegel-type quadratic model {Im⁡w−H(z,z)∈C}\{\operatorname{Im}w-H(z,z)\in C\}{Imw−H(z,z)∈C} to which Vey's theorem applies.

Formalization scope

  • VVV is IsAffineAlgebraic, the zero set of a set of complex polynomials in Fin n → ℂ; U⊆VU\subseteq VU⊆V is open in the subspace topology of VVV, IsConnected (hence nonempty), bounded, and semialgebraic through an inductive predicate on real polynomials in (Re⁡z,Im⁡z)(\operatorname{Re}z,\operatorname{Im}z)(Rez,Imz), closed under complement and union.
  • Γ\GammaΓ is an arbitrary group with the discrete topology acting on the subtype UUU, with ProperSMul and a compact orbit space. Each γ\gammaγ acts holomorphically in the sense of local ambient analytic extension; since γ−1\gamma^{-1}γ−1 also acts, the elements act by biholomorphisms. A non-faithful action is allowed and is harmless, since properness forces finite stabilizers.
  • IsSmooth U asks for local biholomorphisms of neighbourhoods in UUU onto open subsets of some Cm\mathbb C^mCm. IsBoundedSymmetricDomain D asks that DDD be open, connected and bounded, and have at each point an involutive biholomorphism fixing it with that point isolated among its fixed points.
  • The zero-dimensional case (a point) is included and is trivial, matching the source convention.
  • Needed infrastructure: semialgebraic cell decomposition and selection, Montel-type compactness on singular analytic sets, analytic discs, Kobayashi/Carathéodory-type distance estimates and the structure theory of Siegel domains. Each would be reusable.

Selected references

  • J. Vey, Sur la division des domaines de Siegel, Ann. Sci. École Norm. Sup. (4) 3 (1970), 479–506. https://www.numdam.org/article/ASENS_1970_4_3_4_479_0.pdf
  • B. Wong, Characterization of the unit ball in Cn\mathbb C^nCn by its automorphism group, Invent. Math. 41 (1977), 253–257. https://doi.org/10.1007/BF01403050
  • J.-P. Rosay, Sur une caractérisation de la boule parmi les domaines de Cn\mathbb C^nCn par son groupe d'automorphismes, Ann. Inst. Fourier 29 (1979), 91–97. https://doi.org/10.5802/aif.768
  • S. Frankel, Complex geometry of convex domains that cover varieties, Acta Math. 163 (1989), 109–149. https://doi.org/10.1007/BF02392734
  • J. Kollár and J. Pardon, Algebraic varieties with semialgebraic universal cover, J. Topol. 5 (2012), 199–212. https://doi.org/10.1112/jtopol/jts001
  • A. Zimmer, Smoothly bounded domains covering compact manifolds, Indiana Univ. Math. J. 70 (2021), 2653–2676. https://arxiv.org/abs/1910.05288v2
  • M. Coste, Real algebraic sets, lecture notes, 2005. https://indico.ictp.it/event/a04204/session/10/contribution/7/material/0/0.pdf
  • OpenAI, Symmetry of semialgebraic bounded domains with compact quotient, OpenAI Math Release preprint, September 24, 2026 (Theorem 1.1, p. 1). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Symmetry-of-semialgebraic-bounded-domains-with-compact-quotient-September-24-2026/paper.pdf
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Algebraic Geometry·Captain: wurtle

Maximal Seshadri constants on arbitrary polarized surfacesResearch Paper

Motivation: Nagata's conjecture beyond the plane

Seshadri constants measure the local positivity of a line bundle at a point or a configuration of points: how much of an ample class survives after blowing up the points and subtracting equal multiples of the exceptional curves. For rrr points on a surface with ample LLL, a dimension count gives the universal upper bound L2/r\sqrt{L^2/r}L2/r​. Nagata's 1959 conjecture, which came out of his counterexample to Hilbert's fourteenth problem, says that this bound is attained at r≥10r\ge10r≥10 very general points of the projective plane. On an arbitrary polarized surface, the qualitative Nagata–Biran(–Szemberg) conjecture predicts that the bound is attained at rrr very general points for every sufficiently large rrr. On the symplectic side, the conjecture corresponds to packing stability: large numbers of equal balls fill a four-manifold.

Timeline

  • 1959 — Nagata formulates his plane conjecture and proves the strict multiplicity inequality when r≥16r\ge16r≥16 is a perfect square (Amer. J. Math. 81).
  • 1999 — Biran proves symplectic packing stability for rational symplectic classes on closed four-manifolds (Invent. Math. 136).
  • 2003 — Harbourne proves maximality for all sufficiently large rrr with rL2rL^2rL2 a perfect square, and asymptotic lower bounds (J. reine angew. Math.).
  • 2004 — Roé relates one-point and multipoint Seshadri constants (J. Algebra); Strycharz-Szemberg and Szemberg formulate a stronger prediction with an explicit threshold (Serdica Math. J. 30).
  • 2009 — Roé and Ross prove a multipoint product inequality that propagates maximality (Geom. Dedicata).
  • 2010 — Syzdek and Szemberg state the qualitative conjecture for arbitrary polarized surfaces (Math. Nachr. 283, Conjecture 4.3; arXiv:0709.2592).
  • 2016 — Buse, Hind and Opshtein prove strong packing stability for all closed symplectic four-manifolds (Trans. AMS). Symplectic stability alone does not fix the complex structure needed for the algebraic statement (Eckl 2017, Differential Geom. Appl.).
  • 2026 — An OpenAI preprint, Maximal Seshadri constants on arbitrary polarized surfaces (OpenAI Math Release, September 23, 2026), claims the qualitative conjecture for every smooth complex projective surface and every ample line bundle. It has not been peer reviewed, and its proof is not formally verified.

Setting

Let SSS be a smooth integral complex projective surface and LLL an ample line bundle, with self-intersection H=L2H=L^2H=L2. For a tuple p=(p1,…,pr)\mathbf p=(p_1,\dots,p_r)p=(p1​,…,pr​) of distinct points, the multipoint Seshadri constant is

ε(S,L;p)=inf⁡CL⋅C∑i=1rmult⁡piC,\varepsilon(S,L;\mathbf p)=\inf_C\frac{L\cdot C}{\sum_{i=1}^r\operatorname{mult}_{p_i}C},ε(S,L;p)=Cinf​∑i=1r​multpi​​CL⋅C​,

the infimum over integral curves CCC through at least one pip_ipi​. If π:Y→S\pi:Y\to Sπ:Y→S is the blow-up at p\mathbf pp with exceptional curves E1,…,ErE_1,\dots,E_rE1​,…,Er​, then ε\varepsilonε is the largest λ≥0\lambda\ge0λ≥0 such that π∗L−λ∑Ei\pi^*L-\lambda\sum E_iπ∗L−λ∑Ei​ is nef (nonnegative on every integral curve), and always ε≤H/r\varepsilon\le\sqrt{H/r}ε≤H/r​. Let Ur⊂SrU_r\subset S^rUr​⊂Sr be the set of tuples of distinct points. A property holds at very general tuples if it fails only on a countable union of proper Zariski-closed subsets of UrU_rUr​.

Formalization targets

Goal: eventual maximality of multipoint Seshadri constants (Theorem 1.1)

For every such (S,L)(S,L)(S,L) there is r0≥1r_0\ge1r0​≥1 such that for each r≥r0r\ge r_0r≥r0​ there is a countable union ZrZ_rZr​ of proper Zariski-closed subsets of UrU_rUr​, with Ur∖Zr≠∅U_r\setminus Z_r\neq\varnothingUr​∖Zr​=∅, such that for every p∈Ur∖Zr\mathbf p\in U_r\setminus Z_rp∈Ur​∖Zr​

π∗L−L2r∑i=1rEi  is nef on the blow-up at p,andε(S,L;p)=L2r.\pi^*L-\sqrt{\tfrac{L^2}{r}}\sum_{i=1}^rE_i\ \text{ is nef on the blow-up at }\mathbf p, \qquad\text{and}\qquad \varepsilon(S,L;\mathbf p)=\sqrt{\tfrac{L^2}{r}} .π∗L−rL2​​i=1∑r​Ei​  is nef on the blow-up at p,andε(S,L;p)=rL2​​.

The threshold r0r_0r0​ may depend on (S,L)(S,L)(S,L) and is not explicit. The goal statement is published on the platform with status Open.

Significance

The result itself. Theorem 1.1 settles the qualitative Nagata–Biran conjecture for all polarized complex surfaces, with no assumption that rL2rL^2rL2 is a square or that a plane constant is maximal (the hypotheses needed by Harbourne, Roé and Roé–Ross). Because the conclusion is nefness of the square-zero boundary class, it rules out every curve whose degree-to-multiplicity ratio is below L2/r\sqrt{L^2/r}L2/r​, including curves with unequal multiplicities. Via Eckl's Kähler packing correspondence, it gives the algebraic counterpart of packing stability. The sharp plane case (r≥10r\ge10r≥10 in P2\mathbb P^2P2) is a separate companion result.

Formalizing it. The statement is built from scheme-theoretic foundations: projective surfaces, line bundles, sheaf cohomology, blow-ups by universal property, intersection numbers through Euler characteristics. A complete proof would be among the first machine-checked results about the birational geometry of surfaces. Much of the required infrastructure, such as Riemann–Roch on surfaces, finite-dimensionality of coherent cohomology and the existence of blow-ups, is not yet in Mathlib.

Difficulty

The upper bound ε≤H/r\varepsilon\le\sqrt{H/r}ε≤H/r​ is an elementary jet count. Equality requires showing that no curve on the surface has too large multiplicity at the chosen points, for all multiplicity orders simultaneously. Degenerating the points to a special configuration, the usual approach in the plane, fails because the threshold in rrr must be uniform in the jet order: the asymptotic interpolation theorems (Alexander–Hirschowitz) give thresholds depending on a fixed multiplicity bound. Arbitrary surfaces also lack the toric coordinates available on P2\mathbb P^2P2. The source transfers the problem to finite interpolation on an algebraic torus through a nodal divisor in ∣dL∣|dL|∣dL∣.

Formalization scope

  • Surface: an integral scheme, smooth of relative dimension 222 over Spec⁡C\operatorname{Spec}\mathbb CSpecC, with a closed immersion into some PCN\mathbb P^N_{\mathbb C}PCN​ compatible with the structure maps. LineBundle: a locally free sheaf of rank one. IsAmple: the nonvanishing loci of sections of positive powers that are affine form a neighbourhood basis.
  • Cohomology is Ext^n(𝒪, M) in the category of sheaves of modules, and dimensions are finrank ℂ. Then L2=χ(2L)−2χ(L)+χ(O)L^2=\chi(2L)-2\chi(L)+\chi(\mathcal O)L2=χ(2L)−2χ(L)+χ(O) and L⋅C=χ(L∣C)−χ(OC)L\cdot C=\chi(L|_C)-\chi(\mathcal O_C)L⋅C=χ(L∣C​)−χ(OC​), by Riemann–Roch. If cohomology were infinite-dimensional, finrank would return 000; finite-dimensionality for projective schemes is part of what a solver must prove.
  • Points are C\mathbb CC-points, the configuration space carries the Zariski topology induced from SrS^rSr, and very general sets are indexed by N\mathbb NN (IsClosed, ≠ univ, with a point outside all of them).
  • The multiplicity is the order of the local equation of CCC in the local ring at ppp. The Seshadri constant is the real sInf over integral curves with positive total multiplicity.
  • Nefness is expressed through a blow-up PointBlowup, characterized by its universal property, and the exceptional ideal sheaves O(−Ei)\mathcal O(-E_i)O(−Ei​). The inequality is π∗L⋅C+H/r∑ideg⁡(O(−Ei)∣C)≥0\pi^*L\cdot C+\sqrt{H/r}\sum_i \deg(\mathcal O(-E_i)|_C)\ge0π∗L⋅C+H/r​∑i​deg(O(−Ei​)∣C​)≥0 for every integral curve CCC on the blow-up.
  • Needed infrastructure: coherent cohomology of projective schemes, Riemann–Roch on curves and surfaces, blow-ups of points, jets and principal parts. Contributions building any of these layers are welcome and reusable.

Selected references

  • M. Nagata, On the 14-th problem of Hilbert, Amer. J. Math. 81 (1959), 766–772. https://doi.org/10.2307/2372927
  • P. Biran, A stability property of symplectic packing, Invent. Math. 136 (1999), 123–155. https://doi.org/10.1007/s002220050306
  • B. Harbourne, Seshadri constants and very ample divisors on algebraic surfaces, J. reine angew. Math. (2003). https://doi.org/10.1515/crll.2003.044
  • J. Roé, A relation between one-point and multi-point Seshadri constants, J. Algebra (2004). https://doi.org/10.1016/j.jalgebra.2003.10.009
  • B. Strycharz-Szemberg and T. Szemberg, Remarks on the Nagata conjecture, Serdica Math. J. 30 (2004), 405–430. https://www.math.bas.bg/serdica/2004/2004-405-430.pdf
  • J. Roé and J. Ross, An inequality between multipoint Seshadri constants, Geom. Dedicata (2009). https://doi.org/10.1007/s10711-008-9315-4
  • W. Syzdek and T. Szemberg, Seshadri fibrations of algebraic surfaces, Math. Nachr. 283 (2010), 902–908. https://arxiv.org/abs/0709.2592v1
  • O. Buse, R. Hind and E. Opshtein, Packing stability for symplectic 4-manifolds, Trans. Amer. Math. Soc. (2016). https://doi.org/10.1090/tran/6802
  • T. Eckl, Kähler packings and Seshadri constants on projective complex surfaces, Differential Geom. Appl. (2017). https://doi.org/10.1016/j.difgeo.2017.03.007
  • OpenAI, Maximal Seshadri constants on arbitrary polarized surfaces, OpenAI Math Release preprint, September 23, 2026 (Theorem 1.1, p. 2). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/Maximal-Seshadri-Constants-on-Arbitrary-Polarized-Surfaces-September-23-2026/main.pdf
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Number Theory·Captain: wurtle

An asymptotic formula for the number of totientsResearch Paper

Motivation

Euler's totient φ(n)\varphi(n)φ(n) counts the integers in {1,…,n}\{1,\dots,n\}{1,…,n} coprime to nnn. Many integers share a totient, and most integers are not totients at all, so counting the distinct values of φ\varphiφ is much harder than counting integers with a given factorization. Let

V={φ(n):n≥1},V(x)=#{v∈V:v≤x}.\mathcal V=\{\varphi(n):n\ge1\},\qquad V(x)=\#\{v\in\mathcal V: v\le x\}.V={φ(n):n≥1},V(x)=#{v∈V:v≤x}.

The size of V(x)V(x)V(x) was studied by Pillai, Erdős, Hall, Pomerance, Maier and Ford over most of the twentieth century; Ford's 1998 estimate determines V(x)V(x)V(x) up to a bounded multiplicative factor but leaves open whether V(x)V(x)V(x) has an asymptotic equivalent, and Erdős and Hall's question whether V(cx)/V(x)→cV(cx)/V(x)\to cV(cx)/V(x)→c.

This mission asks for a formal proof of the explicit asymptotic formula and the Erdős–Hall regular-variation property, as stated in an OpenAI preprint dated September 25, 2026 (source). The preprint has not been peer reviewed and its claims have not been independently verified; on the platform the Lean goal is open.

Background

  • 1929 — Pillai proves that totients have density zero (Bull. AMS 35, 1929).
  • 1935 — Erdős shows V(x)≪εx/(log⁡x)1−εV(x)\ll_\varepsilon x/(\log x)^{1-\varepsilon}V(x)≪ε​x/(logx)1−ε via the normal number of prime factors of p−1p-1p−1 (Q. J. Math. 1935); later lower bounds use semiprimes (1945).
  • 1976 — Erdős and Hall obtain a lower factor exp⁡{a(log⁡3x)2}\exp\{a(\log_3x)^2\}exp{a(log3​x)2} and ask whether V(cx)/V(x)→cV(cx)/V(x)\to cV(cx)/V(x)→c (Mathematika 1976).
  • 1986–1988 — Pomerance brings the upper bound to the same scale (Acta Arith. 1986); Maier and Pomerance determine the leading constant C0=0.8178…C_0=0.8178\ldotsC0​=0.8178… in V(x)=xlog⁡xexp⁡{(C0+o(1))(log⁡3x)2}V(x)=\frac{x}{\log x}\exp\{(C_0+o(1))(\log_3x)^2\}V(x)=logxx​exp{(C0​+o(1))(log3​x)2} (Acta Arith. 1988).
  • 1998 — Ford determines V(x)V(x)V(x) up to a bounded factor and proves V(cx)−V(x)≍cV(x)V(cx)-V(x)\asymp_cV(x)V(cx)−V(x)≍c​V(x) (Ramanujan J. 1998).
  • September 2026 — The OpenAI preprint claims an explicit asymptotic equivalent and V(cx)/V(x)→cV(cx)/V(x)\to cV(cx)/V(x)→c (Theorem 2.1, p. 4).

Setting

Logarithms are natural and log⁡j\log_jlogj​ is the jjj-fold iterate. For j≥1j\ge1j≥1 let aj=(j+1)log⁡(j+1)−jlog⁡j−1a_j=(j+1)\log(j+1)-j\log j-1aj​=(j+1)log(j+1)−jlogj−1, let ρ∈(0,1)\rho\in(0,1)ρ∈(0,1) be the unique root of ∑j≥1ajρj=1\sum_{j\ge1}a_j\rho^j=1∑j≥1​aj​ρj=1, and set

λ=log⁡(1/ρ),γ=(∑j≥1jajρj)−1,g0=1,  gj=∑d=1jadgj−d.\lambda=\log(1/\rho),\qquad \gamma=\Bigl(\sum_{j\ge1}ja_j\rho^j\Bigr)^{-1},\qquad g_0=1,\ \ g_j=\sum_{d=1}^ja_dg_{j-d}.λ=log(1/ρ),γ=(j≥1∑​jaj​ρj)−1,g0​=1,  gj​=d=1∑j​ad​gj−d​.

For large xxx put B=log⁡2xB=\log_2xB=log2​x, m=⌊(log⁡B−log⁡2B)/λ⌋m=\lfloor(\log B-\log_2B)/\lambda\rfloorm=⌊(logB−log2​B)/λ⌋, the phase θ=(log⁡B−log⁡2B)/λ−m∈[0,1)\theta=(\log B-\log_2B)/\lambda-m\in[0,1)θ=(logB−log2​B)/λ−m∈[0,1), and Gm=Bm/(m!∏i≤mgi)G_m=B^m/(m!\prod_{i\le m}g_i)Gm​=Bm/(m!∏i≤m​gi​); the factor xlog⁡xGm\frac{x}{\log x}G_mlogxx​Gm​ is Ford's counting scale.

For each HHH the preprint defines an explicit arithmetic coefficient AH(f;s)A_H(f;s)AH​(f;s), s∈[0,1)s\in[0,1)s∈[0,1), from a finite set of "tail witnesses" (tuples of primes QhQ_hQh​, P≤h<HP\le h<HP≤h<H, and a cofactor aaa, subject to explicit size and recurrence constraints, P=⌊log⁡log⁡H⌋P=\lfloor\log\log H\rfloorP=⌊loglogH⌋) by inclusion–exclusion over witnesses with a common totient ddd, weighted by f(ℓ(d)/d)/df(\ell(d)/d)/df(ℓ(d)/d)/d (formula (2.8), p. 5). Here ℓ(v)=min⁡{n≥1:φ(n)=v}\ell(v)=\min\{n\ge1:\varphi(n)=v\}ℓ(v)=min{n≥1:φ(n)=v} is the least preimage, and A(f;s)=lim⁡H→∞AH(f;s)A(f;s)=\lim_{H\to\infty}A_H(f;s)A(f;s)=limH→∞​AH​(f;s) when the limit exists. The coefficient is defined without reference to VVV.

Formalization targets

Milestone: nonnegativity of the approximants (Theorem 2.1, p. 4)

AH(1;s)≥0A_H(1;s)\ge0AH​(1;s)≥0 for s∈[0,1)s\in[0,1)s∈[0,1).

Goal: Theorem 2.1 (p. 4)

AH(1;⋅)→A(1;⋅)A_H(1;\cdot)\to A(1;\cdot)AH​(1;⋅)→A(1;⋅) uniformly on [0,1)[0,1)[0,1), with 0<inf⁡sA(1;s)≤sup⁡sA(1;s)<∞0<\inf_sA(1;s)\le\sup_sA(1;s)<\infty0<infs​A(1;s)≤sups​A(1;s)<∞, and

V(x)∼xlog⁡x Gm A(1;θ),V(cx)V(x)⟶c(c>0 fixed).V(x)\sim\frac{x}{\log x}\,G_m\,A(1;\theta),\qquad \frac{V(cx)}{V(x)}\longrightarrow c\quad(c>0\ \text{fixed}).V(x)∼logxx​Gm​A(1;θ),V(x)V(cx)​⟶c(c>0 fixed).

Further milestones: least preimages (Theorem 2.2, p. 6)

For k≥1k\ge1k≥1 let Nk(x)=#{v∈V:v≤x, kx<ℓ(v)≤(k+1)x}N_k(x)=\#\{v\in\mathcal V:v\le x,\ kx<\ell(v)\le(k+1)x\}Nk​(x)=#{v∈V:v≤x, kx<ℓ(v)≤(k+1)x} and fk(r)=min⁡{1,k+1r}−min⁡{1,kr}f_k(r)=\min\{1,\tfrac{k+1}r\}-\min\{1,\tfrac kr\}fk​(r)=min{1,rk+1​}−min{1,rk​}. Then AH(fk;⋅)A_H(f_k;\cdot)AH​(fk​;⋅) converges uniformly and Nk(x)=xlog⁡xGm(A(fk;θ)+o(1))N_k(x)=\frac{x}{\log x}G_m(A(f_k;\theta)+o(1))Nk​(x)=logxx​Gm​(A(fk​;θ)+o(1)); if some totient ddd has ℓ(d)>kd\ell(d)>kdℓ(d)>kd then inf⁡sA(fk;s)>0\inf_sA(f_k;s)>0infs​A(fk​;s)>0 and Nk(x)≍V(x)N_k(x)\asymp V(x)Nk​(x)≍V(x), and otherwise Nk≡0N_k\equiv0Nk​≡0 and A(fk;⋅)≡0A(f_k;\cdot)\equiv0A(fk​;⋅)≡0. The positive alternative holds for k=1,2k=1,2k=1,2.

Significance

The result itself. The formula replaces Ford's bounded uncertainty by an explicit function of the phase θ\thetaθ, built from finite arithmetic data, and answers the Erdős–Hall question: VVV is regularly varying of index 111. Theorem 2.2 answers, in weighted form, Erdős's question on how least preimages are distributed in intervals (kx,(k+1)x](kx,(k+1)x](kx,(k+1)x], reducing it to the existence of a "seed" totient with ℓ(d)>kd\ell(d)>kdℓ(d)>kd. Whether such seeds exist for every kkk (equivalently, whether ℓ(d)/d\ell(d)/dℓ(d)/d is unbounded) is not settled by the preprint.

Formalizing it. This is analytic number theory at full strength: Ford's structure theorems for totient preimages, sieve bounds for two or three linear forms in shifted primes, simplex volume computations and an inclusion–exclusion limit. Mathlib has the totient and the prime number theorem in some forms but not these sieve and distribution results. The definitions in this mission (the renewal root ρ\rhoρ, the witnesses, AHA_HAH​) are explicit and can be checked independently of any proof.

Difficulty

Counting integers nnn with a typical factorization is a volume computation, but V(x)V(x)V(x) counts values, and different nnn can have the same totient. The preprint must show that, outside a negligible set, distinct long prime prefixes give distinct values (Proposition 5.1, p. 22, and Proposition 5.3, p. 27), which needs a uniform comparison estimate for products of shifted primes (Proposition 4.2, p. 17), while short discrete tails that may collide are kept exactly and handled by inclusion–exclusion (Lemma 6.4, p. 30). The limits are taken in a specific order (x→∞x\to\inftyx→∞ with HHH fixed, then H→∞H\to\inftyH→∞), and no continuity of s↦A(1;s)s\mapsto A(1;s)s↦A(1;s) is available, so all errors must be uniform in the phase.

Formalization scope

  • IsTotient v means v=φ(n)v=\varphi(n)v=φ(n) for some n≥1n\ge1n≥1; V x counts totients in {1,…,⌊x⌋}\{1,\dots,\lfloor x\rfloor\}{1,…,⌊x⌋}; ell v is Nat.find of the least preimage (junk value 000 for non-totients, never used on them).
  • rho is the sInf of roots in (0,1)(0,1)(0,1) of ∑j≥0aj+1zj+1=1\sum_{j\ge0}a_{j+1}z^{j+1}=1∑j≥0​aj+1​zj+1=1; the root is unique, so this is the source's ρ\rhoρ.
  • AH H f s is formula (2.8) with finsum over totients ddd and over nonempty finite sets TTT of witnesses with φ(w)=d\varphi(w)=dφ(w)=d; the witness set is finite for each HHH, so the finsums are genuine finite sums. A f s is limUnder atTop; the goal asserts uniform convergence, so the junk value is never used.
  • mainTerm x = x / log x * G x (m x) * A 1 (theta x); asymptotic equivalence is Tendsto (V x / mainTerm x) atTop (𝓝 1).
  • The milestone coefficient_nonnegative is stated for every HHH, while the source states it for sufficiently large HHH; for small HHH the witness set is empty or the inclusion–exclusion is still a probability, so this is a harmless strengthening.
  • companion_zero_case restates alternative (ii) of Theorem 2.2 in a separate definition file (TotientCompanionZero) with identical definitions.

Selected references

  • OpenAI, An asymptotic formula for the number of totients, OpenAI Math Release preprint, September 25, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/An-asymptotic-formula-for-the-number-of-totients-September-25-2026/An-asymptotic-formula-for-the-number-of-totients-September-25-2026.pdf
  • S. S. Pillai, On some functions connected with φ(n), Bull. Amer. Math. Soc. 35 (1929), 832–836. https://www.ams.org/journals/bull/1929-35-06/S0002-9904-1929-04799-2/S0002-9904-1929-04799-2.pdf
  • P. Erdős, On the normal number of prime factors of p−1 and some related problems concerning Euler's φ-function, Q. J. Math. 6 (1935). https://doi.org/10.1093/qmath/os-6.1.205
  • P. Erdős, R. R. Hall, Distinct values of Euler's φ-function, Mathematika 23 (1976). https://doi.org/10.1112/S0025579300006100
  • C. Pomerance, On the distribution of the values of Euler's function, Acta Arith. 47 (1986). https://doi.org/10.4064/aa-47-1-63-70
  • H. Maier, C. Pomerance, On the number of distinct values of Euler's φ-function, Acta Arith. 49 (1988). https://doi.org/10.4064/aa-49-3-263-275
  • K. Ford, The distribution of totients, Ramanujan J. 2 (1998), 67–151; revised arXiv:1104.3264. https://arxiv.org/abs/1104.3264v2
2 thms1 active userReviewed
Number Theory·Captain: wurtle

A quadratic bound for Jacobsthal's functionResearch Paper

Motivation

For a positive integer nnn, Jacobsthal's function j(n)j(n)j(n) is the least mmm such that every run of mmm consecutive integers contains an integer coprime to nnn. Equivalently, j(n)−1j(n)-1j(n)−1 is the longest interval that can be covered by choosing one residue class modulo each prime divisor of nnn (the classes 0 mod p0\bmod p0modp shifted by the interval's start). Its maximum over integers with at most kkk prime divisors,

h(k)=max⁡{j(n): ω(n)≤k},h(k)=\max\{j(n):\ \omega(n)\le k\},h(k)=max{j(n): ω(n)≤k},

governs how long a stretch can be sieved out by kkk primes. It is linked to gaps between consecutive primes (long covered intervals produce large prime gaps, as in the Erdős–Rankin and Ford–Green–Konyagin–Maynard–Tao constructions) and to the limits of the linear sieve. Jacobsthal asked, and Erdős recorded, whether h(k)≪k2h(k)\ll k^2h(k)≪k2.

Background

  • 1960. Jacobsthal begins a series of papers on this function.
  • 1962. Erdős records Jacobsthal's question h(k)≪k2h(k)\ll k^2h(k)≪k2 and notes Brun's bound h(k)≪kC0h(k)\ll k^{C_0}h(k)≪kC0​ (doi:10.7146/math.scand.a-10523).
  • 1971. Iwaniec's error-term estimate for the linear sieve gives order k2log⁡2kk^2\log^2kk2log2k for the first kkk primes (doi:10.4064/aa-19-1-1-30).
  • 1977. Vaughan proves the uniform bound j(n)≪ω(n)2log⁡(2ω(n))j(n)\ll\omega(n)^2\log(2\omega(n))j(n)≪ω(n)2log(2ω(n)) and explains the exponent 2 by the linear sieve's square-root restriction (doi:10.1017/S0013091500026560).
  • 1978. Iwaniec proves h(k)≪k2log⁡2kh(k)\ll k^2\log^2kh(k)≪k2log2k for arbitrary prime sets (doi:10.1515/dema-1978-0121).
  • 2012. Hajdu and Saradha disprove Jacobsthal's primorial-extremality conjecture: j(P24)=234<236=h(24)j(P_{24})=234<236=h(24)j(P24​)=234<236=h(24) (doi:10.1090/S0025-5718-2012-02581-6).
  • 2015, 2019. Costello–Watts and Ziller compute bounds and exact values for small kkk (doi:10.1090/S0025-5718-2014-02896-2).
  • 2018. Ford, Green, Konyagin, Maynard and Tao give the lower bound j(Pk)≫k(log⁡k)2log⁡log⁡log⁡klog⁡log⁡kj(P_k)\gg k(\log k)^2\frac{\log\log\log k}{\log\log k}j(Pk​)≫k(logk)2loglogklogloglogk​ (doi:10.1090/jams/876).

The source of this mission is an OpenAI preprint dated September 25, 2026.

Setting

  • ω(n)\omega(n)ω(n) is the number of distinct prime divisors of nnn, with ω(1)=0\omega(1)=0ω(1)=0.
  • For n≥1n\ge1n≥1, j(n)j(n)j(n) is the least m≥1m\ge1m≥1 such that for every integer aaa, one of a,a+1,…,a+m−1a,a+1,\dots,a+m-1a,a+1,…,a+m−1 is coprime to nnn (note gcd⁡(0,n)=n\gcd(0,n)=ngcd(0,n)=n).
  • h(k)=sup⁡{j(n):n≥1, ω(n)≤k}h(k)=\sup\{j(n):n\ge1,\ \omega(n)\le k\}h(k)=sup{j(n):n≥1, ω(n)≤k} for k≥1k\ge1k≥1. All logarithms are natural.

Formalization targets

Milestone: quadratic bound (Jacobsthal's question)

∃C>0 ∀k≥1:h(k)≤C k2.\exists C>0\ \forall k\ge1:\quad h(k)\le C\,k^2.∃C>0 ∀k≥1:h(k)≤Ck2.

Goal: Theorem 1.1

∃C>0 ∀k≥1:h(k)≤C k2(log⁡log⁡(3k))2.\exists C>0\ \forall k\ge1:\quad h(k)\le C\,\frac{k^2}{(\log\log(3k))^2}.∃C>0 ∀k≥1:h(k)≤C(loglog(3k))2k2​.

Both are stated in Lean as the existence, for each kkk, of a length mmm satisfying IsJacobsthalBound k m with the displayed size bound. The goal OAI.Erdos970.Erdos970Final.erdos_970_iterated_log is open on the platform.

Significance

Theorem 1.1 answers Jacobsthal's question affirmatively, uniformly over all prime sets and interval positions, and removes the logarithmic losses in the bounds of Vaughan and Iwaniec, going slightly below the quadratic scale. It shows that the linear sieve can be pushed to its limiting parameter s=2s=2s=2, where the lower sieve function vanishes, by retaining a boundary contribution. The gap to the best lower bound (roughly k(log⁡k)2k(\log k)^2k(logk)2) remains large; Vaughan suggested j(n)≪εω(n)1+εj(n)\ll_\varepsilon\omega(n)^{1+\varepsilon}j(n)≪ε​ω(n)1+ε.

The result is proved in an OpenAI preprint, which has not been peer reviewed. No machine-checked proof exists. The constant CCC is not made explicit.

Difficulty

The linear sieve gives a lower bound for the number of uncovered integers only when the sieving range is below the square root of the interval length, which is exactly where the exponent 2 comes from; at the critical parameter the main term of the lower-bound sieve vanishes, so the standard argument yields nothing and a log⁡2k\log^2klog2k loss appears. One must keep the boundary term that the leading linear-sieve calculation discards, and then show that the actual prescribed residue classes do not deviate much from a reference calculation. Positivity of the reference calculation alone does not suffice: the comparison requires an inverse estimate for a witnessing edge of a decreasing-prime tree, variance bounds, and stopping-time counts for hard endpoints.

Formalization scope

  • IsJacobsthalBound k m: for every n : ℕ with 0 < n and n.primeFactors.card ≤ k, and every a : ℤ, some i < m has (a + i).natAbs.Coprime n.
  • JacobsthalQuadratic: ∃ C > 0, ∀ k > 0, ∃ m, IsJacobsthalBound k m ∧ m ≤ C k^2.
  • JacobsthalIteratedLog: the same with bound C k^2 / (log (log (3k)))^2; for k≥1k\ge1k≥1 the denominator is positive.
  • The two targets live in separate definition files that both define IsJacobsthalBound identically.

A complete development needs the fundamental lemma of the sieve, Buchstab-type functions of the linear sieve, prime number theorem estimates in short ranges and progressions, the additive large sieve, a lattice-point count on plane curves, and renewal-type arguments for a continuous path process. Contributions formalizing Theorem 1.2 (the quantitative covering estimate), Lemma 2.1 (fundamental lemma of the sieve), Proposition 6.3 (positive reference margin), Proposition 8.2 (inverse estimate for an edge) and Corollary 9.3 are welcome.

Selected references

  • OpenAI, A quadratic bound for Jacobsthal's function, preprint, September 25, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/A-quadratic-bound-for-Jacobsthals-function-September-25-2026/paper.pdf
  • P. Erdős, On the integers relatively prime to n and on a number-theoretic function considered by Jacobsthal, Math. Scand., 1962. https://doi.org/10.7146/math.scand.a-10523
  • R. C. Vaughan, On the order of magnitude of Jacobsthal's function, Proc. Edinburgh Math. Soc., 1977. https://doi.org/10.1017/S0013091500026560
  • H. Iwaniec, On the problem of Jacobsthal, Demonstratio Math., 1978. https://doi.org/10.1515/dema-1978-0121
  • H. Iwaniec, On the error term in the linear sieve, Acta Arith., 1971. https://doi.org/10.4064/aa-19-1-1-30
  • L. Hajdu, N. Saradha, Disproof of a conjecture of Jacobsthal, Math. Comp., 2012. https://doi.org/10.1090/S0025-5718-2012-02581-6
  • K. Ford, B. Green, S. Konyagin, J. Maynard, T. Tao, Long gaps between primes, J. Amer. Math. Soc., 2018. https://doi.org/10.1090/jams/876
  • F. Costello, P. Watts, An upper bound on Jacobsthal's function, Math. Comp., 2015. https://doi.org/10.1090/S0025-5718-2014-02896-2
  • J. Friedlander, H. Iwaniec, Opera de Cribro, AMS, 2010. https://doi.org/10.1090/coll/057
2 thms1 active userReviewed
CombinatoricsNumber Theory·Captain: wurtle

The additive indecomposability of the primesResearch Paper

Motivation

Goldbach-type problems ask which sets are sumsets of primes. The inverse Goldbach problem, due to Ostmann (1956), reverses the question: can the set of primes itself, up to finitely many changes, be written as a sumset A+B={a+b:a∈A, b∈B}A+B=\{a+b:a\in A,\ b\in B\}A+B={a+b:a∈A, b∈B} with both AAA and BBB having at least two elements? Ostmann conjectured that it cannot: the primes are asymptotically additively indecomposable. The problem is a clean test of how much additive structure the primes can carry, and it connects sieve theory (the primes avoid one residue class modulo each prime) with inverse questions for the large sieve.

Background

  • 1954–1955. Hornfeck proves early restrictions on additive decompositions of the primes (doi:10.1007/BF01187376).
  • 1956. Ostmann formulates the conjecture in his treatise on additive number theory (doi:10.1007/978-3-662-11030-0).
  • 1964. Laffer and Mann show that a hypothetical decomposition must have two infinite summands (doi:10.2140/pjm.1964.14.547).
  • 1988–1996. Pomerance, Sárközy and Stewart, and Hofmann and Wolke, obtain sieve restrictions on the summands (doi:10.2140/pjm.1988.133.363, doi:10.1007/BF01189097).
  • 2001. Elsholtz combines the large and larger sieves to put both counting functions near the square-root scale and rules out decompositions into three nontrivial summands (doi:10.1112/S0025579300014406).
  • 2014–2016. Green and Harper show that an inverse large-sieve conjecture would imply Ostmann's conjecture (arXiv:1311.6176); Elsholtz and Harper sharpen the binary counting bounds (doi:10.1090/S0002-9947-2014-06384-8); Shao proves a finite ternary obstruction (doi:10.1353/ajm.2016.0038).
  • 2020–2026. Hanson, then Croot, Mao and Yip, and Croot, Mao, Pohoata and Yip prove partial inverse theorems for the large sieve and derive further necessary conditions on a hypothetical decomposition (doi:10.1017/S0305004118000518, arXiv:2510.08862, arXiv:2607.15311).

The source of this mission is an OpenAI preprint dated September 24, 2026.

Setting

Let N0={0,1,2,… }\mathbb N_0=\{0,1,2,\dots\}N0​={0,1,2,…} and let PPP be the set of positive primes. For A,B⊆N0A,B\subseteq\mathbb N_0A,B⊆N0​ the sumset is A+B={a+b:a∈A, b∈B}A+B=\{a+b:a\in A,\ b\in B\}A+B={a+b:a∈A, b∈B}. Two sets are asymptotically equal if their symmetric difference X△Y=(X∖Y)∪(Y∖X)X\triangle Y=(X\setminus Y)\cup(Y\setminus X)X△Y=(X∖Y)∪(Y∖X) is finite. A decomposition is nontrivial if ∣A∣≥2|A|\ge2∣A∣≥2 and ∣B∣≥2|B|\ge2∣B∣≥2; the trivial decompositions {0}+P\{0\}+P{0}+P are excluded.

Formalization targets

Goal: Theorem 1.1 (Ostmann's conjecture)

A,B⊆N0, ∣A∣≥2, ∣B∣≥2 ⟹ (A+B)△P is infinite.A,B\subseteq\mathbb N_0,\ |A|\ge2,\ |B|\ge2\ \Longrightarrow\ (A+B)\triangle P\ \text{is infinite}.A,B⊆N0​, ∣A∣≥2, ∣B∣≥2 ⟹ (A+B)△P is infinite.

Milestone: Theorem 2.3 (two infinite summands)

There are no infinite A,B⊆N0 with (A+B)△P finite.\text{There are no infinite }A,B\subseteq\mathbb N_0\text{ with }(A+B)\triangle P\text{ finite}.There are no infinite A,B⊆N0​ with (A+B)△P finite.

Theorem 2.3 is the core of the proof; Lemma 2.2 reduces Theorem 1.1 to it by a short sieve argument.

The goal OAI.Ostmann.inverseGoldbach is open on the platform.

Significance

Theorem 1.1 resolves Ostmann's conjecture. The conclusion controls both requirements of an eventual decomposition: if ∣A∣,∣B∣≥2|A|,|B|\ge2∣A∣,∣B∣≥2 and A+BA+BA+B contains every sufficiently large prime, then A+BA+BA+B contains infinitely many composite numbers. The proof does not go through the general inverse large-sieve conjecture of Green and Harper; it uses the simultaneous residue restrictions together with coverage of every large prime directly.

The result is proved in an OpenAI preprint, which has not been peer reviewed. No machine-checked proof exists. The statement is elementary, so the formal target is short; the difficulty is entirely in the proof.

Difficulty

Counting alone cannot work: sieve bounds put A(x)A(x)A(x) and B(x)B(x)B(x) near x\sqrt xx​, which is compatible with the lower bound A(x)B(x)≫x/log⁡xA(x)B(x)\gg x/\log xA(x)B(x)≫x/logx forced by covering the primes. The known necessary conditions (large intersections with quadratic images) fall short of the near-containment that Green and Harper's conditional route requires. The proof must turn the local information (modulo each prime ppp, the images of AAA and −B-B−B are disjoint) into a global contradiction: it rules out correlations with translated multiplicative characters of every order, compares a positive sumset statistic with its average over the primes, and handles residue indicators with no prescribed algebraic form via a finite-field comparison of binary trees. The paper runs to about 80 pages.

Formalization scope

  • primes = {n : ℕ | Nat.Prime n}; sumset A B is Mathlib's pointwise A + B on Set ℕ.
  • A.Nontrivial means AAA has two distinct elements.
  • InverseGoldbach asserts Set.Infinite (sumset A B ∆ primes); TwoInfiniteSummandsImpossible uses EventuallyPrimeSumset A B := ∃ N, ∀ n ≥ N, n ∈ A + B ↔ n.Prime, which is equivalent to a finite symmetric difference.
  • OAI.Ostmann.main is the same statement as the goal without the definition file and is included as a reference item.

A complete development needs the additive large sieve (Montgomery–Vaughan), Gallagher's larger sieve, Mertens-type estimates, character sums over finite fields including the quadratic large sieve, prime number theorems in progressions with the exceptional character, and Fourier analysis on Fp\mathbb F_pFp​. Contributions formalizing Lemma 2.1 (fixed shifts), Lemma 2.2 (finite summands), Lemma 2.4 (square-root bounds), Proposition 3.1, Corollary 4.2 (mixed-character decorrelation), Proposition 5.1 and Lemma 6.1 (tree comparison) are welcome.

Selected references

  • OpenAI, The additive indecomposability of the primes, preprint, September 24, 2026. https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/the-additive-indecomposability-of-the-primes-September-24-2026/paper.pdf
  • H.-H. Ostmann, Additive Zahlentheorie. Erster Teil, Springer, 1956. https://doi.org/10.1007/978-3-662-11030-0
  • W. B. Laffer, H. B. Mann, Decomposition of sets of group elements, Pacific J. Math., 1964. https://doi.org/10.2140/pjm.1964.14.547
  • C. Elsholtz, The inverse Goldbach problem, Mathematika, 2001. https://doi.org/10.1112/S0025579300014406
  • B. Green, A. J. Harper, Inverse questions for the large sieve, Geom. Funct. Anal., 2014. https://doi.org/10.1007/s00039-014-0288-1
  • C. Elsholtz, A. J. Harper, Additive decompositions of sets with restricted prime factors, Trans. Amer. Math. Soc., 2015. https://doi.org/10.1090/S0002-9947-2014-06384-8
  • X. Shao, On an inverse ternary Goldbach problem, Amer. J. Math., 2016. https://doi.org/10.1353/ajm.2016.0038
  • B. Hanson, Additive correlation and the inverse problem for the large sieve, Math. Proc. Cambridge Philos. Soc., 2020. https://doi.org/10.1017/S0305004118000518
  • E. Croot, J. Mao, C. Pohoata, C. H. Yip, A sharp inverse theorem for the quadratic large sieve, preprint, 2026. https://arxiv.org/abs/2607.15311
  • H. L. Montgomery, R. C. Vaughan, The large sieve, Mathematika, 1973. https://doi.org/10.1112/S0025579300004708
4 thms1 active userReviewed
Number TheoryProbability·Captain: wurtle

The joint Dickman law for consecutive integersResearch Paper

Motivation: largest prime factors of neighbouring integers

Let P+(n)P^+(n)P+(n) be the largest prime factor of an integer n≥2n\ge2n≥2. The classical theory of smooth numbers (Dickman 1930, Ramaswami 1949, de Bruijn 1951) shows that log⁡P+(n)/log⁡n\log P^+(n)/\log nlogP+(n)/logn has a limiting distribution: the proportion of n≤Xn\le Xn≤X with P+(n)≤XaP^+(n)\le X^aP+(n)≤Xa tends to ρ(1/a)\rho(1/a)ρ(1/a), where ρ\rhoρ is the Dickman function. Multiplicative structure of nnn and of n+1n+1n+1 is expected to be independent, since consecutive integers share no prime factor, but proving independence of such "additive shifts" of multiplicative data is the core difficulty of the Chowla/Elliott circle of problems. Erdős and Pomerance (1978) asked whether P+(n)P^+(n)P+(n) and P+(n+1)P^+(n+1)P+(n+1) are asymptotically independent with Dickman marginals, and, as a consequence, whether P+(n)<P+(n+1)P^+(n)<P^+(n+1)P+(n)<P+(n+1) holds for exactly half of all nnn (a comparison question usually attributed to Erdős and Turán).

Timeline

  • 1930–1951 — Dickman, Ramaswami and de Bruijn establish the one-variable law #{n≤X:P+(n)≤Xa}/X→ρ(1/a)\#\{n\le X: P^+(n)\le X^a\}/X\to\rho(1/a)#{n≤X:P+(n)≤Xa}/X→ρ(1/a).
  • 1978 — Erdős and Pomerance formulate the joint independence problem, prove that each ordering of P+(n),P+(n+1)P^+(n),P^+(n+1)P+(n),P+(n+1) has lower natural density at least 0.00990.00990.0099, and show that P+(n)/P+(n+1)P^+(n)/P^+(n+1)P+(n)/P+(n+1) rarely lies in (X−δ,Xδ)(X^{-\delta},X^{\delta})(X−δ,Xδ) (Aequationes Math. 17 (1978)).
  • 2005 — de la Bretèche, Pomerance and Tenenbaum raise the lower density to 0.055440.055440.05544 (and 0.058660.058660.05866 via an observation of Fouvry).
  • 2017–2018 — Wang obtains 0.10630.10630.1063 and 0.13560.13560.1356.
  • 2018 — Teräväinen proves the joint Dickman law in logarithmic density, and logarithmic density 1/21/21/2 for the ordering (Forum Math. Sigma 6 (2018)).
  • 2019 — Tao and Teräväinen obtain the joint law for ordinary averages outside an exceptional set of scales of logarithmic density zero (Algebra Number Theory 13 (2019)).
  • 2021 — Wang proves the ordinary joint law conditionally on the Elliott–Halberstam conjecture for friable integers (J. Number Theory 223).
  • 2022 — Jiang, Lü and Wang prove averaged-over-shift versions (Adv. Math. 409).
  • 2025–2026 — Lü and Wang reach lower density 0.20170.20170.2017; Yang reaches 0.2800.2800.280 (arXiv:2607.16032); Tao and Teräväinen give a quantitative joint law outside exceptional scales (arXiv:2512.01739).
  • 2026 — An OpenAI preprint, The joint Dickman law for consecutive integers (OpenAI Math Release, September 24, 2026), claims the unconditional joint law in ordinary natural density at every scale. It has not been peer reviewed, and its proof is not formally verified.

Setting

For n≥2n\ge2n≥2, P+(n)P^+(n)P+(n) is the largest prime dividing nnn. The Dickman function ρ:[0,∞)→R\rho:[0,\infty)\to\mathbb Rρ:[0,∞)→R is the continuous function with

ρ(u)=1 (0≤u≤1),uρ′(u)=−ρ(u−1) (u>1),\rho(u)=1\ (0\le u\le1),\qquad u\rho'(u)=-\rho(u-1)\ (u>1),ρ(u)=1 (0≤u≤1),uρ′(u)=−ρ(u−1) (u>1),

equivalently ρ(u)=1−∫1uρ(t−1) dtt\rho(u)=1-\int_1^u \rho(t-1)\,\frac{dt}{t}ρ(u)=1−∫1u​ρ(t−1)tdt​ for u≥1u\ge1u≥1. For a property PPP of integers and real X>0X>0X>0, the natural density at scale XXX is

realDensity(P,X)=1X #{2≤n≤X: P(n)}.\mathrm{realDensity}(P,X)=\frac1X\,\#\{2\le n\le X:\ P(n)\}.realDensity(P,X)=X1​#{2≤n≤X: P(n)}.

Formalization targets

Goal: the joint Dickman law (Theorem 1.1)

For every fixed a,b∈(0,1)a,b\in(0,1)a,b∈(0,1),

lim⁡X→∞1X#{2≤n≤X: P+(n)≤na, P+(n+1)≤nb}=ρ(1/a) ρ(1/b),\lim_{X\to\infty}\frac1X\#\{2\le n\le X:\ P^+(n)\le n^a,\ P^+(n+1)\le n^b\}=\rho(1/a)\,\rho(1/b),X→∞lim​X1​#{2≤n≤X: P+(n)≤na, P+(n+1)≤nb}=ρ(1/a)ρ(1/b),

with XXX running over all reals and ordinary, unweighted counting. The goal statement is published on the platform with status Open.

Milestones: equal ordering densities (Corollary 1.2)

lim⁡X→∞1X#{2≤n≤X: P+(n)<P+(n+1)}=12,lim⁡X→∞1X#{2≤n≤X: P+(n+1)<P+(n)}=12.\lim_{X\to\infty}\frac1X\#\{2\le n\le X:\ P^+(n)<P^+(n+1)\}=\tfrac12, \qquad \lim_{X\to\infty}\frac1X\#\{2\le n\le X:\ P^+(n+1)<P^+(n)\}=\tfrac12 .X→∞lim​X1​#{2≤n≤X: P+(n)<P+(n+1)}=21​,X→∞lim​X1​#{2≤n≤X: P+(n+1)<P+(n)}=21​.

Significance

The result itself. Theorem 1.1 says that, in natural density, log⁡P+(n)/log⁡n\log P^+(n)/\log nlogP+(n)/logn and log⁡P+(n+1)/log⁡n\log P^+(n+1)/\log nlogP+(n+1)/logn are independent with Dickman marginals. It settles the Erdős–Pomerance independence question positively, removes the logarithmic weighting of Teräväinen's theorem and the exceptional scales of Tao–Teräväinen, and implies the Erdős–Turán comparison: because the limiting marginal is continuous, the product law gives no mass to the diagonal, so each strict ordering has density 1/21/21/2 (Corollary 1.2). Previous unconditional work only gave lower natural densities, never the existence of the density.

Formalizing it. The goal is a clean density statement about elementary objects, but its proof combines Matomäki–Radziwiłł short-interval estimates, sieve bounds, a divisor amplifier, graph comparison and cut-norm sampling. No part of this argument is machine-checked. Even the one-variable Dickman law (the marginal case) is not in Mathlib, and would be a reusable milestone in its own right.

Difficulty

Independence of nnn and n+1n+1n+1 is a binary correlation problem for multiplicative data, of the same nature as the two-point Chowla conjecture. Tao's entropy-decrement and logarithmic-averaging method handles such correlations only with weight 1/n1/n1/n, or at almost all scales; the step that fails for ordinary density is ruling out a positive correlation along a sparse sequence of scales. The indicator 1P+(n)≤na\mathbf 1_{P^+(n)\le n^a}1P+(n)≤na​ is also not multiplicative, so short-interval theorems for multiplicative functions do not apply to it directly.

Formalization scope

  • Nat.maxPrimeFac n (largest element of the prime factor list, a faithful backport of the Mathlib definition) represents P+(n)P^+(n)P+(n); its junk values at 0,10,10,1 are irrelevant since counting starts at n=2n=2n=2.
  • realDensity P X is #{n ∈ [2, ⌊X⌋] : P n} / X as a real number, and limits are Tendsto … atTop over real XXX, matching "the limit over all real XXX".
  • The thresholds are P+(n)≤naP^+(n)\le n^aP+(n)≤na with real powers (n:ℝ)^a, and the hypotheses 0<a<10<a<10<a<1, 0<b<10<b<10<b<1 are explicit.
  • ρ\rhoρ is constructed by iterating the delay integral equation stepApprox and evaluating the ⌈u⌉\lceil u\rceil⌈u⌉-th iterate at uuu; this agrees with the Dickman function on [0,∞)[0,\infty)[0,∞), which is the only range used (1/a,1/b>11/a,1/b>11/a,1/b>1). A wrong ρ\rhoρ would make the goal false, so this definition is part of what solvers should check.
  • Needed infrastructure: the one-variable Dickman law, Selberg–Delange and sieve estimates, short-interval mean values of multiplicative functions, and profinite/Haar-measure compactness arguments. Contributions formalizing the smooth-number marginal or the deduction of Corollary 1.2 from Theorem 1.1 are welcome.

Selected references

  • K. Dickman, On the frequency of numbers containing prime factors of a certain relative magnitude, Ark. Mat. Astr. Fys. 22A (1930).
  • N. G. de Bruijn, On the number of positive integers ≤x\le x≤x and free of prime factors >y>y>y, Proc. KNAW Ser. A 54 (1951). https://research.tue.nl/en/publications/on-the-number-of-positive-integers-leq-x-and-free-of-prime-factor/
  • P. Erdős and C. Pomerance, On the largest prime factors of nnn and n+1n+1n+1, Aequationes Math. 17 (1978), 311–321. https://www.renyi.hu/~p_erdos/1978-29.pdf
  • J. Teräväinen, On binary correlations of multiplicative functions, Forum Math. Sigma 6 (2018), e10. https://doi.org/10.1017/fms.2018.10
  • T. Tao and J. Teräväinen, The structure of correlations of multiplicative functions at almost all scales, with applications to the Chowla and Elliott conjectures, Algebra Number Theory 13 (2019). https://doi.org/10.2140/ant.2019.13.2103
  • K. Matomäki and M. Radziwiłł, Multiplicative functions in short intervals, Ann. of Math. 183 (2016). https://doi.org/10.4007/annals.2016.183.3.6
  • T. Tao and J. Teräväinen, Quantitative correlations and some problems on prime factors of consecutive integers, preprint (2026). https://arxiv.org/abs/2512.01739
  • OpenAI, The joint Dickman law for consecutive integers, OpenAI Math Release preprint, September 24, 2026 (Theorem 1.1 and Corollary 1.2, p. 2). https://github.com/openai/math/blob/adc7f1241b42e322a6451854ab7e4b4c146bf78a/preprints/The-joint-Dickman-law-for-consecutive-integers-September-24-2026/paper.pdf
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Law of Large Numbers Limits for Many-Server Queues 1: The Fluid Equations Have at Most One Solution, Given Explicitly by the Age Representation (3.11)Research Paper

Motivation

Large service systems such as call centers and hospital wards are modelled as many-server queues: NNN identical servers, customers arriving according to a general process, service requirements drawn independently from a general distribution GGG, and a single first-come-first-served queue. When GGG is not exponential the number of customers in system is not Markov, and a tractable state must keep track of how long each customer in service has been served. Kaspi and Ramanan (Ann. Appl. Probab. 21 (2011)) take as state the number in system together with the age measure, the point measure of the ages of the customers in service, and prove a functional law of large numbers: scaled by NNN, these processes converge to the unique solution of a deterministic system, the fluid equations. Earlier fluid and diffusion analyses of the G/GI/NG/GI/NG/GI/N queue worked with other state descriptors (Reed, Ann. Appl. Probab. 19 (2009); Whitt, Oper. Res. 54 (2006)); the measure-valued description records the elapsed service time of every customer in service, which is the information a non-exponential service distribution requires.

This mission covers the deterministic half of that result: the fluid equations are well posed, and their solution is given in closed form.

Setting

Service requirements have a density ggg that vanishes on (−∞,0)(-\infty,0)(−∞,0), G(x)=∫(−∞,x]gG(x)=\int_{(-\infty,x]}gG(x)=∫(−∞,x]​g, and the mean is normalized to one, ∫x g(x) dx=1\int x\,g(x)\,dx=1∫xg(x)dx=1. Let M=sup⁡{x≥0:G(x)<1}∈(0,∞]M=\sup\{x\ge0: G(x)<1\}\in(0,\infty]M=sup{x≥0:G(x)<1}∈(0,∞] and let h=g/(1−G)h=g/(1-G)h=g/(1−G) be the hazard rate on [0,M)[0,M)[0,M); hhh is locally integrable on [0,M)[0,M)[0,M) but not integrable on it. For a measure μ\muμ and a function fff write ⟨f,μ⟩=∫f dμ\langle f,\mu\rangle=\int f\,d\mu⟨f,μ⟩=∫fdμ, and 1\mathbf 11 for the constant one.

The data are a triple (Eˉ,Xˉ(0),νˉ0)(\bar E,\bar X(0),\bar\nu_0)(Eˉ,Xˉ(0),νˉ0​) in

S0={(f,x,μ):f nondecreasing caˋdlaˋg,f(0)=0; x≥0; μ a measure on [0,M), ⟨1,μ⟩≤1, 1−⟨1,μ⟩=[1−x]+},\mathcal S_0=\{(f,x,\mu): f \text{ nondecreasing càdlàg}, f(0)=0;\ x\ge0;\ \mu \text{ a measure on }[0,M),\ \langle\mathbf 1,\mu\rangle\le1,\ 1-\langle\mathbf 1,\mu\rangle=[1-x]^+\},S0​={(f,x,μ):f nondecreasing caˋdlaˋg,f(0)=0; x≥0; μ a measure on [0,M), ⟨1,μ⟩≤1, 1−⟨1,μ⟩=[1−x]+},

where Eˉ\bar EEˉ is the cumulative arrival process, Xˉ(0)\bar X(0)Xˉ(0) the initial number in system and νˉ0\bar\nu_0νˉ0​ the initial age measure (per server, so the total capacity is one). A càdlàg pair (Xˉ,νˉ)(\bar X,\bar\nu)(Xˉ,νˉ), with νˉt\bar\nu_tνˉt​ a sub-probability measure on [0,M)[0,M)[0,M) in the weak topology, solves the fluid equations if for every t≥0t\ge0t≥0: ∫0t⟨h,νˉs⟩ds<∞\int_0^t\langle h,\bar\nu_s\rangle ds<\infty∫0t​⟨h,νˉs​⟩ds<∞ (3.4); for every test function φ∈Cc1,1([0,M)×R+)\varphi\in\mathcal C_c^{1,1}([0,M)\times\mathbb R_+)φ∈Cc1,1​([0,M)×R+​)

⟨φ(⋅,t),νˉt⟩=⟨φ(⋅,0),νˉ0⟩+∫0t⟨φx+φs,νˉs⟩ds−∫0t⟨hφ(⋅,s),νˉs⟩ds+∫[0,t]φ(0,s) dKˉ(s)(3.5);\langle\varphi(\cdot,t),\bar\nu_t\rangle=\langle\varphi(\cdot,0),\bar\nu_0\rangle+\int_0^t\langle\varphi_x+\varphi_s,\bar\nu_s\rangle ds-\int_0^t\langle h\varphi(\cdot,s),\bar\nu_s\rangle ds+\int_{[0,t]}\varphi(0,s)\,d\bar K(s)\quad(3.5);⟨φ(⋅,t),νˉt​⟩=⟨φ(⋅,0),νˉ0​⟩+∫0t​⟨φx​+φs​,νˉs​⟩ds−∫0t​⟨hφ(⋅,s),νˉs​⟩ds+∫[0,t]​φ(0,s)dKˉ(s)(3.5);

Xˉ(t)=Xˉ(0)+Eˉ(t)−Dˉ(t)\bar X(t)=\bar X(0)+\bar E(t)-\bar D(t)Xˉ(t)=Xˉ(0)+Eˉ(t)−Dˉ(t) (3.6); and the nonidling condition 1−⟨1,νˉt⟩=[1−Xˉ(t)]+1-\langle\mathbf 1,\bar\nu_t\rangle=[1-\bar X(t)]^+1−⟨1,νˉt​⟩=[1−Xˉ(t)]+ (3.7). Here Dˉ(t)=∫0t⟨h,νˉs⟩ds\bar D(t)=\int_0^t\langle h,\bar\nu_s\rangle dsDˉ(t)=∫0t​⟨h,νˉs​⟩ds is the cumulative departure process and Kˉ(t)=⟨1,νˉt⟩−⟨1,νˉ0⟩+Dˉ(t)\bar K(t)=\langle\mathbf 1,\bar\nu_t\rangle-\langle\mathbf 1,\bar\nu_0\rangle+\bar D(t)Kˉ(t)=⟨1,νˉt​⟩−⟨1,νˉ0​⟩+Dˉ(t) the cumulative entry into service. Equation (3.5) is a weak form of a transport equation: mass moves to the right at unit speed, is killed at rate hhh, and enters at age 000 at rate dKˉd\bar KdKˉ.

Formalization targets

Goal: Theorem 3.5

For every (Eˉ,Xˉ(0),νˉ0)∈S0(\bar E,\bar X(0),\bar\nu_0)\in\mathcal S_0(Eˉ,Xˉ(0),νˉ0​)∈S0​:

  1. the fluid equations have at most one solution;
  2. under (3.4) and the path conditions, (Xˉ,νˉ)(\bar X,\bar\nu)(Xˉ,νˉ) is a solution if and only if it satisfies (3.6), (3.7) and, for every bounded continuous fff and t≥0t\ge0t≥0,
⟨f,νˉt⟩=∫[0,M)f(x+t)1−G(x+t)1−G(x) νˉ0(dx)+∫[0,t]f(t−s)(1−G(t−s)) dKˉ(s);(3.11)\langle f,\bar\nu_t\rangle=\int_{[0,M)}f(x+t)\frac{1-G(x+t)}{1-G(x)}\,\bar\nu_0(dx)+\int_{[0,t]}f(t-s)\big(1-G(t-s)\big)\,d\bar K(s);\quad(3.11)⟨f,νˉt​⟩=∫[0,M)​f(x+t)1−G(x)1−G(x+t)​νˉ0​(dx)+∫[0,t]​f(t−s)(1−G(t−s))dKˉ(s);(3.11)
  1. if Eˉ\bar EEˉ has a density λˉ\bar\lambdaλˉ, then Kˉ\bar KKˉ has a density κˉ\bar\kappaκˉ equal a.e. to λˉ\bar\lambdaλˉ where Xˉ<1\bar X<1Xˉ<1, to λˉ∧⟨h,νˉt⟩\bar\lambda\wedge\langle h,\bar\nu_t\rangleλˉ∧⟨h,νˉt​⟩ where Xˉ=1\bar X=1Xˉ=1, and to ⟨h,νˉt⟩\langle h,\bar\nu_t\rangle⟨h,νˉt​⟩ where Xˉ>1\bar X>1Xˉ>1 (3.12);
  2. if moreover νˉ0\bar\nu_0νˉ0​ is absolutely continuous, so is every νˉt\bar\nu_tνˉt​.

Milestones

  • Remark 4.3, (4.4): integration by parts for the entry term of (4.3).
  • (4.55): the integrated hazard in closed form, ψh(x,t)=(1−G(x))/(1−G(x−t))\psi_h(x,t)=(1-G(x))/(1-G(x-t))ψh​(x,t)=(1−G(x))/(1−G(x−t)) or 1−G(x)1-G(x)1−G(x).
  • Theorem 4.1: for a Radon-measure-valued path satisfying the hazard bound (4.1), the age equation (4.2), which is (3.5) with an arbitrary Radon measure υ0\upsilon_0υ0​ and an arbitrary ZZZ of bounded variation in place of νˉ0\bar\nu_0νˉ0​ and Kˉ\bar KKˉ, holds if and only if the representation (4.3) holds.
  • Proof of Corollary 4.4: Kˉ\bar KKˉ is nondecreasing.
  • Corollary 4.4, (4.5): Kˉ(t)=⟨1,νˉt⟩−⟨1,νˉ0⟩+∫G(x+t)−G(x)1−G(x)νˉ0(dx)+∫0tg(t−s)Kˉ(s)ds\bar K(t)=\langle\mathbf 1,\bar\nu_t\rangle-\langle\mathbf 1,\bar\nu_0\rangle+\int\frac{G(x+t)-G(x)}{1-G(x)}\bar\nu_0(dx)+\int_0^tg(t-s)\bar K(s)dsKˉ(t)=⟨1,νˉt​⟩−⟨1,νˉ0​⟩+∫1−G(x)G(x+t)−G(x)​νˉ0​(dx)+∫0t​g(t−s)Kˉ(s)ds.
  • Lemma 4.5: ∥⟨f,νˉs2⟩−⟨f,νˉs1⟩∥T≤∥f∥M∣Δυ0∣TV+(2∥f∥T+∥f′∥T)∥ΔZ∥T\|\langle f,\bar\nu^2_s\rangle-\langle f,\bar\nu^1_s\rangle\|_T\le\|f\|_M|\Delta\upsilon_0|_{TV}+(2\|f\|_T+\|f'\|_T)\|\Delta Z\|_T∥⟨f,νˉs2​⟩−⟨f,νˉs1​⟩∥T​≤∥f∥M​∣Δυ0​∣TV​+(2∥f∥T​+∥f′∥T​)∥ΔZ∥T​.
  • Theorem 4.6: with equal initial measures, ∥ΔKˉ∥T∨∥ΔDˉ∥T≤∣ΔXˉ(0)∣+∥ΔEˉ∥T\|\Delta\bar K\|_T\vee\|\Delta\bar D\|_T\le|\Delta\bar X(0)|+\|\Delta\bar E\|_T∥ΔKˉ∥T​∨∥ΔDˉ∥T​≤∣ΔXˉ(0)∣+∥ΔEˉ∥T​, together with (4.8) and (4.10).

Significance

Uniqueness of the fluid solution is what turns tightness of the scaled NNN-server processes into convergence: every subsequential limit solves the fluid equations, so all coincide (the paper's Theorem 3.7). The representation (3.11) reduces the measure-valued equation to the scalar process Kˉ\bar KKˉ, and the continuity estimate of Theorem 4.6 says the fluid solution is a Lipschitz function of the arrival process. Both are used in the paper's study of long-time behaviour, where νˉt\bar\nu_tνˉt​ converges to the measure with density 1−G1-G1−G, and (3.12) is the form of the entry rate used there.

The results are proved in the paper. None of them, and no part of the measure-valued fluid model, has a machine-checked proof. Formalizing them produces a checked weak-solution theory for a transport equation with an unbounded killing rate and a measure-valued boundary input, which is a reusable piece of infrastructure for other age- and residual-time-based queueing models.

Difficulty

The central step is Theorem 4.1. The naive approach treats (4.2) as a first-order PDE and integrates along characteristics, but the solution is only a càdlàg path of measures, hhh is merely locally integrable and blows up near MMM, and ZZZ may jump, so classical characteristics are not available; the test functions must not vanish on the boundary x=0x=0x=0, since that is where the entry term lives. The second difficulty is in Theorem 4.6: the entry process Kˉ\bar KKˉ is defined implicitly through the nonidling condition, and comparing two solutions requires a first-crossing argument that distinguishes whether the system is below, at or above capacity at that time.

Formalization scope

The service law is its density ggg (ServiceLaw), with g=0g=0g=0 below 000, ∫g=1\int g=1∫g=1 and mean one. Time is R\mathbb RR read on [0,∞)[0,\infty)[0,∞); every condition is stated for t≥0t\ge0t≥0. Measures of the fluid model are FiniteMeasure ℝ carried by [0,M)[0,M)[0,M), whose topology is weak convergence, so càdlàg paths are càdlàg in MF[0,M)\mathcal M_F[0,M)MF​[0,M) with the weak topology. The paths of Theorem 4.1 and Lemma 4.5 are ℝ → Measure ℝ with values Radon on [0,M)[0,M)[0,M) (possibly infinite) and càdlàg in the vague topology. M∈[0,∞]M\in[0,\infty]M∈[0,∞] is an extended number. The integral in (3.4) is a lower integral in [0,∞][0,\infty][0,∞], and Dˉ\bar DDˉ, Kˉ\bar KKˉ are its real value. dKˉd\bar KdKˉ is the Lebesgue–Stieltjes measure of Kˉ\bar KKˉ, with no atom at 000. A function ZZZ of bounded variation is a difference Z1−Z2Z_1-Z_2Z1​−Z2​ of nondecreasing càdlàg functions vanishing at 000.

Explicit choices: compact support of test functions is relative to [0,M)×R+[0,M)\times\mathbb R_+[0,M)×R+​, so φ(0,s)\varphi(0,s)φ(0,s) need not vanish (with supports taken in R2\mathbb R^2R2 the entry term of (3.5) would vanish identically); νˉ(0)=νˉ0\bar\nu(0)=\bar\nu_0νˉ(0)=νˉ0​ and Xˉ(0)\bar X(0)Xˉ(0) equal to the datum are clauses of the fluid equations, but not of the age equation, where υ0\upsilon_0υ0​ is arbitrary; functions in Cb(R+)\mathcal C_b(\mathbb R_+)Cb​(R+​), Cc(R+)\mathcal C_c(\mathbb R_+)Cc​(R+​) and Cb1(R+)\mathcal C^1_b(\mathbb R_+)Cb1​(R+​) are represented by functions on R\mathbb RR of the same class, of which only values on [0,∞)[0,\infty)[0,∞) are read; norms in Lemma 4.5 are computed in [0,∞][0,\infty][0,∞], since ∣Δυ0∣TV|\Delta\upsilon_0|_{TV}∣Δυ0​∣TV​ may be infinite. Two printed statements are corrected: in Theorem 3.5 the paper's right-hand side of the equivalence lists (3.6) and (3.11); the nonidling condition (3.7), part of the fluid equations on the left, is kept on the right, as the proof requires. In (4.10), ΔXˉ(0)\Delta\bar X(0)ΔXˉ(0) is replaced by ∣ΔXˉ(0)∣|\Delta\bar X(0)|∣ΔXˉ(0)∣, which is what Lemma 4.5 and (4.9) give.

A fluid solution is defined by the weak transport equation (3.5), never by the representation (3.11) or by a formula for νˉ\bar\nuνˉ in terms of Kˉ\bar KKˉ; with such a definition the equivalence of the goal would be an unfolding of definitions.

A complete development needs Lebesgue–Stieltjes integration by parts for càdlàg functions of bounded variation, the Riesz description of the vague topology, and uniqueness for weak solutions of transport equations; these are reusable beyond this mission. Proofs of any milestone, and of the parts of Theorem 3.5 separately, are welcome. The paper's Section 4.3 machinery (the abstract and simplified age equations, Lemmas 4.12–4.13, Propositions 4.15–4.16) is not posed here and may be formalized as supporting lemmas.

Selected references

  • H. Kaspi and K. Ramanan, Law of large numbers limits for many-server queues, Ann. Appl. Probab. 21(1) (2011), 33–114. https://doi.org/10.1214/09-AAP662
  • J. Reed, The G/GI/N queue in the Halfin–Whitt regime, Ann. Appl. Probab. 19(6) (2009), 2211–2269. https://doi.org/10.1214/09-AAP609
  • W. Whitt, Fluid models for multiserver queues with abandonments, Oper. Res. 54(1) (2006), 37–54. https://doi.org/10.1287/opre.1050.0227
  • S. Asmussen, Applied Probability and Queues, 2nd ed., Springer, 2003. https://doi.org/10.1007/b97236
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CombinatoricsOperations ResearchProbability+1·Captain: mikedeng1

Phase Transition of the Largest Eigenvalue for Nonnull Complex Sample Covariance Matrices 4: The Exponential Last Passage Time Has the Law of the Largest Sample EigenvalueResearch Paper

Motivation

Random growth models and random matrices share limit laws. The first exact instance was found by Johansson (Shape fluctuations and random matrices, Comm. Math. Phys. 2000), who showed that the last passage time of a lattice model with geometric or exponential weights has the law of the largest eigenvalue of a Laguerre (complex Wishart) random matrix. Baik, Ben Arous and Péché (Ann. Probab. 33 (2005)) extended the identity to weights whose rate depends on the row. On the matrix side this is a sample covariance matrix with a general population covariance Σ\SigmaΣ; on the growth side it is a corner growth model, or a series of exponential queues, with inhomogeneous service rates.

The identity is the reason the paper's main result, the phase transition of the largest sample eigenvalue as a few population eigenvalues ("spikes") cross the critical value 1+γ−11 + \gamma^{-1}1+γ−1, is also a theorem about last passage percolation and tandem queues. In the queueing reading, a spike is a slow server, and the phase transition describes how slow a few servers must be before they change the centring and the fluctuation scale of the exit time.

Timeline:

  • 2000, Johansson: equal rates; the geometric and exponential last passage time has the law of the largest Laguerre eigenvalue (his Proposition 1.4 is the case π1=⋯=πN\pi_1 = \cdots = \pi_Nπ1​=⋯=πN​ of (307)).
  • 2001, Baryshnikov and, independently, Gravner, Tracy and Widom: the tandem-queue and GUE-minor descriptions of the same object.
  • 2005, Baik, Ben Arous and Péché: row-dependent rates πi\pi_iπi​ (Proposition 6.1), via the Robinson–Schensted–Knuth (RSK) formula for geometric weights (310) and a scaling limit.

Setting

Last passage time. Attach a real weight X(i,j)X(i,j)X(i,j) to every site of the grid {1,…,N}×{1,…,M}\{1,\ldots,N\}\times\{1,\ldots,M\}{1,…,N}×{1,…,M}. An up/right path from (1,1)(1,1)(1,1) to (N,M)(N,M)(N,M) is a sequence of N+M−1N+M-1N+M−1 sites starting at (1,1)(1,1)(1,1), ending at (N,M)(N,M)(N,M), each step adding (1,0)(1,0)(1,0) or (0,1)(0,1)(0,1). The last passage time is

L(N,M)=max⁡π:(1,1)↗(N,M)∑(i,j)∈πX(i,j).(306)L(N,M) = \max_{\pi:(1,1)\nearrow(N,M)} \sum_{(i,j)\in\pi} X(i,j). \qquad (306)L(N,M)=π:(1,1)↗(N,M)max​(i,j)∈π∑​X(i,j).(306)

Exponential environment. Given positive numbers π1,…,πN\pi_1,\ldots,\pi_Nπ1​,…,πN​, the X(i,j)X(i,j)X(i,j) are independent and X(i,j)X(i,j)X(i,j) is exponential of mean 1/(πiM)1/(\pi_iM)1/(πi​M), i.e. density πiMe−πiMx\pi_iMe^{-\pi_iMx}πi​Me−πi​Mx on x≥0x\ge0x≥0. All MMM sites of row iii share one rate.

Sample covariance matrix. Let gkjg_{kj}gkj​, 1≤k≤M1\le k\le M1≤k≤M, 1≤j≤N1\le j\le N1≤j≤N, be independent standard complex Gaussians (real and imaginary parts independent N(0,1/2)N(0,1/2)N(0,1/2)). For a unitary UUU and ℓj=πj−1\ell_j = \pi_j^{-1}ℓj​=πj−1​ (308), the samples y⃗k=U diag(ℓj) gk\vec y_k = U\,\mathrm{diag}(\sqrt{\ell_j})\,g_ky​k​=Udiag(ℓj​​)gk​ are mean-zero complex Gaussian vectors with covariance Σ=U diag(ℓ) U∗\Sigma = U\,\mathrm{diag}(\ell)\,U^*Σ=Udiag(ℓ)U∗, and

S=1M∑k=1My⃗ky⃗k ∗,λ1=largest eigenvalue of S.S = \frac1M\sum_{k=1}^M \vec y_k\vec y_k^{\,*}, \qquad \lambda_1 = \text{largest eigenvalue of } S.S=M1​k=1∑M​y​k​y​k∗​,λ1​=largest eigenvalue of S.

Schur functions and geometric weights. For a partition λ\lambdaλ, the Schur function sλ(x)s_\lambda(x)sλ​(x) is the sum over semistandard Young tableaux TTT of shape λ\lambdaλ of ∏cxT(c)\prod_{c}x_{T(c)}∏c​xT(c)​. A geometric variable of parameter q∈[0,1)q\in[0,1)q∈[0,1) has P(Y=k)=(1−q)qk\mathbb P(Y = k) = (1-q)q^kP(Y=k)=(1−q)qk.

Formalization targets

Goal: Proposition 6.1

For 1≤N≤M1\le N\le M1≤N≤M, positive π1,…,πN\pi_1,\ldots,\pi_Nπ1​,…,πN​ and every unitary UUU,

P(L(N,M)≤x)=P(λ1(M,N)≤x)for all x∈R.(309)\mathbb P\big(L(N,M)\le x\big) = \mathbb P\big(\lambda_1(M,N)\le x\big) \qquad \text{for all } x\in\mathbb R. \qquad (309)P(L(N,M)≤x)=P(λ1​(M,N)≤x)for all x∈R.(309)

The two sides are defined independently: one by a maximum over lattice paths of exponential weights, the other by the spectrum of a Gaussian random matrix.

Milestones

  1. The recurrence (313): for every array of weights and every site with a,b≥2a, b\ge2a,b≥2,
L(a,b)=max⁡{L(a−1,b),L(a,b−1)}+X(a,b).L(a,b) = \max\{L(a-1,b), L(a,b-1)\} + X(a,b).L(a,b)=max{L(a−1,b),L(a,b−1)}+X(a,b).
  1. The Cauchy identity (311): ∑λsλ(x)sλ(y)=∏i,j(1−xiyj)−1\sum_\lambda s_\lambda(x)s_\lambda(y) = \prod_{i,j}(1-x_iy_j)^{-1}∑λ​sλ​(x)sλ​(y)=∏i,j​(1−xi​yj​)−1 for xi,yj≥0x_i,y_j\ge0xi​,yj​≥0, xiyj<1x_iy_j<1xi​yj​<1.
  2. The geometric formula (310): for independent geometric weights Y(i,j)Y(i,j)Y(i,j) of parameter xiyjx_iy_jxi​yj​, xi,yj∈[0,1)x_i,y_j\in[0,1)xi​,yj​∈[0,1),
P(G(N,M)≤n)=∏i,j(1−xiyj)∑λ:λ1≤nsλ(x)sλ(y).\mathbb P\big(G(N,M)\le n\big) = \prod_{i,j}(1-x_iy_j)\sum_{\lambda:\lambda_1\le n}s_\lambda(x)s_\lambda(y).P(G(N,M)≤n)=i,j∏​(1−xi​yj​)λ:λ1​≤n∑​sλ​(x)sλ​(y).
  1. The exponential formula (307): for M≥NM\ge NM≥N and distinct πi\pi_iπi​,
P(L(N,M)≤x)=1C∫[0,x]Ndet⁡(e−Mπiξj)V(π)V(ξ)∏jξjM−N dξ.\mathbb P\big(L(N,M)\le x\big) = \frac1C\int_{[0,x]^N}\frac{\det(e^{-M\pi_i\xi_j})}{V(\pi)}V(\xi)\prod_{j}\xi_j^{M-N}\,d\xi.P(L(N,M)≤x)=C1​∫[0,x]N​V(π)det(e−Mπi​ξj​)​V(ξ)j∏​ξjM−N​dξ.

Significance

The proposition makes every distributional statement about λ1\lambda_1λ1​ in the paper a statement about last passage percolation with row-dependent rates: the FkF_kFk​ (generalised Tracy–Widom) fluctuations at the critical spike value, the Gaussian fluctuations above it, and the fixed-dimension limit. Through (312)–(313) it also covers the exit time of MMM customers from NNN exponential servers in series, an operations research object. The geometric formula (310) is the entry point of the RSK method for exactly solvable growth models.

These results are proved in the literature; the paper cites (310) and (311) and derives (307) from them by a limit. None of them is machine-checked as far as this series knows. Formalizing them requires a Lean development of Schur functions, the Cauchy identity and the RSK correspondence on matrices with nonnegative integer entries, and of the Wishart eigenvalue density, each reusable well beyond this mission.

Difficulty

The recurrence (313) is elementary. Everything else is not. The geometric formula (310) needs the RSK bijection between nonnegative integer matrices and pairs of semistandard tableaux of the same shape, together with Schensted's theorem that the first row of the shape is the last passage time; neither is in Mathlib. The passage from (310) to (307) is a scaling limit xi=1−Mπi/Lx_i = 1 - M\pi_i/Lxi​=1−Mπi​/L, n=xLn = xLn=xL, L→∞L\to\inftyL→∞, in which a sum over partitions must converge to a multiple integral. The matrix side needs the joint eigenvalue density of a complex Wishart matrix with general Σ\SigmaΣ, which uses the Harish-Chandra–Itzykson–Zuber integral. A direct coupling of the two sides is not known; the identity is an equality of laws, proved by computing both.

Formalization scope

The development lives in the namespace SpikedWishart.LastPassage. Conventions:

  • Sites and indices are 0-based; the paper's (1,1)(1,1)(1,1) and (N,M)(N,M)(N,M) are (0,0)(0,0)(0,0) and (N−1,M−1)(N-1,M-1)(N−1,M−1), and N,M≥1N, M\ge1N,M≥1.
  • LLL is defined as the maximum over paths (306), never by the recurrence (313), so (313) is a genuine statement.
  • The exponential law is Mathlib's expMeasure with rate πiM\pi_iMπi​M.
  • The sample model is mean zero, uncentred, with factor 1/M1/M1/M and E∣g∣2=1\mathbb E|g|^2 = 1E∣g∣2=1; this is the model of (59), (61) and (307), and the page's centring by the sample mean and S=(1/N)XX∗S = (1/N)XX^*S=(1/N)XX∗ are printed slips. The covariance is U diag(π−1)U∗U\,\mathrm{diag}(\pi^{-1})U^*Udiag(π−1)U∗ for every unitary UUU; specialising to diagonal Σ\SigmaΣ would prove a special case.
  • N≤MN\le MN≤M is a disclosed addition to the goal, matching the page's "for M≥NM\ge NM≥N" in (307).
  • Proposition 6.1 prints L(M,N)L(M,N)L(M,N); the object is (306)'s L(N,M)L(N,M)L(N,M).
  • Schur functions are the tableau sum with variables extended by zero. The Cauchy identity is stated with the exponent −1-1−1 that the page omits. In (307) the constant CCC is the integral of the same integrand over (0,∞)N(0,\infty)^N(0,∞)N, and the πi\pi_iπi​ are distinct so that V(π)≠0V(\pi)\ne0V(π)=0; the goal has no distinctness hypothesis.

A formalization in which either side of (309) is defined through the other, or through (307), would be trivial and is excluded: both laws are defined from scratch. Contributions are welcome on RSK and Schur function infrastructure, on the Wishart density, and on the measurability of λ1\lambda_1λ1​.

Selected references

  • J. Baik, G. Ben Arous, S. Péché, Phase transition of the largest eigenvalue for nonnull complex sample covariance matrices, Ann. Probab. 33(5):1643–1697, 2005. https://doi.org/10.1214/009117905000000233
  • K. Johansson, Shape fluctuations and random matrices, Comm. Math. Phys. 209:437–476, 2000. https://doi.org/10.1007/s002200050027
  • Y. Baryshnikov, GUEs and queues, Probab. Theory Related Fields 119:256–274, 2001. https://doi.org/10.1007/PL00008760
  • J. Gravner, C. A. Tracy, H. Widom, Limit theorems for height fluctuations in a class of discrete space and time growth models, J. Stat. Phys. 102:1085–1132, 2001. https://doi.org/10.1023/A:1004879725949
  • R. P. Stanley, Enumerative Combinatorics, Vol. 2, Cambridge University Press, 1999 (the paper's reference [36] for the Cauchy identity). https://doi.org/10.1017/CBO9780511609589
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Partial Differential EquationsProbability·Captain: mikedeng1

An Optimal Variance Estimate in Stochastic Homogenization of Discrete Elliptic Equations: Scale-L Averages of the Approximate-Corrector Energy Density Have Variance ≲ L^−d, Times (ln T)^q if d = 2Research Paper

Motivation

A random conductance model puts a random conductivity on every edge of the lattice Zd\mathbb Z^dZd. It is the simplest discrete model of diffusion in a heterogeneous medium, and its generator is the generator of a random walk in a random environment. Classical stochastic homogenization shows that on large scales the random operator behaves like a constant-coefficient elliptic operator whose homogenized coefficient AhomA_{\mathrm{hom}}Ahom​ is deterministic. The characterization of AhomA_{\mathrm{hom}}Ahom​, however, involves an equation on the whole lattice for every realization of the coefficients. In practice one computes a spatial average over a box of size LLL of the energy density of an approximate corrector, as in Gloria and Otto (arXiv:1104.1291, §1). The random part of the error of this procedure is a variance. Its size as a function of LLL determines how large a computational box must be.

Timeline:

  • 1979–1981: Kozlov (MR542557) and Papanicolaou–Varadhan (MR712714) prove qualitative homogenization for continuum elliptic equations with random coefficients.
  • 1983: Künnemann (MR714611) proves the discrete version, the diffusion limit of reversible jump processes on Zd\mathbb Z^dZd with ergodic bond conductivities. It provides the corrector and the approximate corrector used here.
  • 1986: Yurinskii (MR867870) gives the first quantitative rates, far from optimal.
  • 1998: Naddaf and Spencer (preprint, Estimates on the variance of some homogenization problems) introduce the spectral-gap approach and obtain optimal variance bounds under a small-contrast assumption on the conductivities.
  • 2011: Gloria and Otto (DOI 10.1214/10-AOP571) prove the optimal variance estimate without any small-contrast assumption, with a logarithmic loss in d=2d = 2d=2. This mission formalizes that estimate.

Setting

Fix d≥2d \ge 2d≥2 and 0<α≤β0 < \alpha \le \beta0<α≤β. Sites are x∈Zdx \in \mathbb Z^dx∈Zd and e1,…,ede_1,\dots,e_de1​,…,ed​ is the canonical basis. An edge [z,z+ei][z, z+e_i][z,z+ei​] carries a conductivity a(z,i)∈[α,β]a(z,i) \in [\alpha,\beta]a(z,i)∈[α,β]; such a field aaa belongs to the class Aαβ\mathcal A_{\alpha\beta}Aαβ​. The discrete gradients are ∇iu(x)=u(x+ei)−u(x)\nabla_i u(x) = u(x+e_i) - u(x)∇i​u(x)=u(x+ei​)−u(x) and ∇i∗u(x)=u(x)−u(x−ei)\nabla^*_i u(x) = u(x) - u(x-e_i)∇i∗​u(x)=u(x)−u(x−ei​), and A(x)=diag[a(x,1),…,a(x,d)]A(x) = \mathrm{diag}[a(x,1),\dots,a(x,d)]A(x)=diag[a(x,1),…,a(x,d)].

The conductivities are i.i.d.: they are distributed according to the product ν⊗E\nu^{\otimes E}ν⊗E over all edges of one probability law ν\nuν on [α,β][\alpha,\beta][α,β]. No density is assumed, so atomic laws are allowed. ⟨⋅⟩\langle\cdot\rangle⟨⋅⟩ is the expectation and var⁡\operatorname{var}var the variance.

For a direction ξ∈Rd\xi \in \mathbb R^dξ∈Rd with ∣ξ∣=1|\xi| = 1∣ξ∣=1 and a cut-off T>0T > 0T>0, the approximate corrector ϕT\phi_TϕT​ solves

T−1ϕT(x)−∇∗⋅A(x)(∇ϕT(x)+ξ)=0(x∈Zd).T^{-1}\phi_T(x) - \nabla^*\cdot A(x)\big(\nabla\phi_T(x) + \xi\big) = 0 \qquad (x \in \mathbb Z^d).T−1ϕT​(x)−∇∗⋅A(x)(∇ϕT​(x)+ξ)=0(x∈Zd).

The zero-order term introduces the length scale T\sqrt TT​. The Green's function GT(x,y;a)G_T(x,y;a)GT​(x,y;a) is the ℓ2\ell^2ℓ2 solution of (T−1−∇∗⋅A∇)GT(⋅,y)=δy(T^{-1} - \nabla^*\cdot A\nabla)G_T(\cdot,y) = \delta_y(T−1−∇∗⋅A∇)GT​(⋅,y)=δy​ in weak form.

A mask ηL:Zd→[0,1]\eta_L : \mathbb Z^d \to [0,1]ηL​:Zd→[0,1] is supported in the box (−L,L)d(-L,L)^d(−L,L)d, sums to 111 and satisfies ∣∇ηL∣≤CηL−d−1|\nabla\eta_L| \le C_\eta L^{-d-1}∣∇ηL​∣≤Cη​L−d−1. The averaged energy density is

ξ⋅AL,Tξ=∑x∈Zd(T−1ϕT(x)2+(∇ϕT(x)+ξ)⋅A(x)(∇ϕT(x)+ξ))ηL(x).\xi\cdot A_{L,T}\xi = \sum_{x\in\mathbb Z^d}\Big(T^{-1}\phi_T(x)^2 + \big(\nabla\phi_T(x)+\xi\big)\cdot A(x)\big(\nabla\phi_T(x)+\xi\big)\Big)\eta_L(x).ξ⋅AL,T​ξ=x∈Zd∑​(T−1ϕT​(x)2+(∇ϕT​(x)+ξ)⋅A(x)(∇ϕT​(x)+ξ))ηL​(x).

Formalization targets

Goal: Theorem 2.1, estimate (2.5)

There are an exponent q>0q > 0q>0 and a threshold T0T_0T0​, depending only on d,α,βd, \alpha, \betad,α,β, and for each mask constant CηC_\etaCη​ a constant CCC, such that for every law ν\nuν on [α,β][\alpha,\beta][α,β], every unit ξ\xiξ, every T≥T0T \ge T_0T≥T0​, every L>0L > 0L>0 and every mask ηL\eta_LηL​:

var⁡[ξ⋅AL,Tξ]≤{C L−2(ln⁡T)q,d=2,C L−d,d>2.\operatorname{var}[\xi\cdot A_{L,T}\xi] \le \begin{cases} C\,L^{-2}(\ln T)^q, & d = 2,\\ C\,L^{-d}, & d > 2.\end{cases}var[ξ⋅AL,T​ξ]≤{CL−2(lnT)q,CL−d,​d=2,d>2.​

The statement fixes the shape of the bound, not the value of qqq or CCC, which is why it is the goal.

Milestones

The milestones follow the paper's proof:

  • well-posedness: the Green's function (Definition 2.7) and the approximate corrector (Lemma 2.2, pathwise form);
  • the variance estimate Lemma 2.3 for functions of countably many i.i.d. variables;
  • deterministic Green's-function bounds: Lemma 2.8 (BMO and decay on dyadic annuli), Lemma 2.9 (Meyers-type higher integrability), Corollaries 2.2 and 2.3;
  • susceptibility formulas: Lemmas 2.4 and 2.5, which give ∂ϕT/∂a(e)\partial\phi_T/\partial a(e)∂ϕT​/∂a(e) and ∂GT/∂a(e)\partial G_T/\partial a(e)∂GT​/∂a(e);
  • measurability: Lemma 2.6;
  • a Caccioppoli inequality in probability: Lemma 2.7;
  • a convolution estimate: Lemma 2.10;
  • moment bounds: Proposition 2.1, which gives ⟨∣ϕT(0)∣q⟩≲1\langle|\phi_T(0)|^q\rangle \lesssim 1⟨∣ϕT​(0)∣q⟩≲1 for d>2d > 2d>2 and ≲(ln⁡T)γ(q)\lesssim(\ln T)^{\gamma(q)}≲(lnT)γ(q) for d=2d = 2d=2;
  • the two steps of §3.2: the derivative formula (3.17) and its uniform bound (3.22).

Significance

The result. The estimate says that smooth averages of the energy density fluctuate as if the density were independent from site to site, at the rate L−d/2L^{-d/2}L−d/2. That rate is the best possible, since the coefficients themselves are independent. In the error analysis of approximations of AhomA_{\mathrm{hom}}Ahom​, this controls the random error and separates it from the systematic error caused by the cut-off TTT. Proposition 2.1, a byproduct, shows that the approximate corrector has all moments bounded uniformly in TTT when d>2d > 2d>2, which yields a stationary corrector (Corollary 2.1 of the paper).

Formalizing it. The theorem has been proved since 2011, and no machine-checked version is known. A formal proof would combine three components that are reusable well beyond this paper:

  • a variance inequality for functions of countably many independent variables, in the infinite product measure;
  • quantitative regularity of discrete elliptic Green's functions, uniform in the coefficients;
  • the sensitivity calculus of solutions with respect to a single coefficient.

Difficulty

The obvious argument fails because the energy density is not independent from site to site: the corrector couples all conductivities through the inverse of a random elliptic operator. Bounding the variance needs the sensitivity of ξ⋅AL,Tξ\xi\cdot A_{L,T}\xiξ⋅AL,T​ξ to each single conductivity, and that sensitivity is expressed through gradients of the Green's function. Pointwise bounds on ∇GT\nabla G_T∇GT​ that are uniform in the coefficients do not exist: the continuum counterpart fails, by examples from quasi-conformal mappings. Only averaged decay on dyadic annuli and a small gain of integrability are available, which is why the proof needs the moment bounds of Proposition 2.1. In d=2d = 2d=2 the Green's function does not decay, so only BMO-type control is available, and a logarithm of TTT is lost.

Formalization scope

Statements follow the arXiv v1 reprint (arXiv:1104.1291v1) of the Annals of Probability article; theorem and page numbers refer to it.

  • The lattice is Fin d → ℤ. An edge [z,z+ei][z,z+e_i][z,z+ei​] is the pair (z, i), and a coefficient field is a real function on edges; the theorems assume it takes values in [α,β][\alpha,\beta][α,β].
  • The i.i.d. law is Measure.infinitePi of one probability measure ν\nuν on R\mathbb RR with ν([α,β]c)=0\nu([\alpha,\beta]^c) = 0ν([α,β]c)=0.
  • ϕT\phi_TϕT​ is the unique bounded solution of (2.3) and GT(⋅,y)G_T(\cdot,y)GT​(⋅,y) the unique ℓ2\ell^2ℓ2 solution of (2.11). The definitions file explains why the bounded solution is the paper's stationary ϕT\phi_TϕT​.
  • Norms are Euclidean. The mask's gradient bound is componentwise with an explicit constant CηC_\etaCη​.
  • Every ≲\lesssim≲ becomes an explicit constant chosen before the law, the coefficients and the spatial variables, and every ≫1\gg 1≫1 becomes an explicit threshold.
  • Variances, lower integrals and infinite sums that could otherwise default to 000 are either taken in [0,∞][0,\infty][0,∞] or come with square-integrability in the conclusion.

Ruling out a trivial formalization. If the constant CCC were allowed to depend on the law ν\nuν or on the mask, the goal would follow from the boundedness of the energy average alone. If the variance were taken of a function that is not square-integrable, it would be 000 by convention. The goal fixes CCC before ν\nuν and ηL\eta_LηL​ and requires square-integrability as part of the conclusion.

Not formalized:

  • the last sentence of Theorem 2.1, the variance estimate for the corrector ϕ\phiϕ itself when d>2d > 2d>2. It needs the stationary corrector of Lemma 2.1, whose existence is quoted from Künnemann;
  • Lemma 2.8 (ii);
  • the stochastic part of Lemma 2.2 (stationarity and zero mean of ϕT\phi_TϕT​), which follows from the pathwise statement as explained in the definitions file.

Welcome contributions are proofs of any milestone and reusable infrastructure: infinite-product variance inequalities, discrete integration by parts, and discrete maximum principles.

Selected references

  • A. Gloria, F. Otto, An optimal variance estimate in stochastic homogenization of discrete elliptic equations, Ann. Probab. 39(3) (2011) 779–856. arXiv:1104.1291, DOI 10.1214/10-AOP571
  • R. Künnemann, The diffusion limit for reversible jump processes on Zd\mathbb Z^dZd with ergodic random bond conductivities, Comm. Math. Phys. 90 (1983) 27–68. MR714611
  • S. M. Kozlov, The averaging of random operators, Mat. Sb. 109(151) (1979) 188–202. MR542557
  • G. C. Papanicolaou, S. R. S. Varadhan, Boundary value problems with rapidly oscillating random coefficients, Colloq. Math. Soc. János Bolyai 27 (1981) 835–873. MR712714
  • V. V. Yurinskii, Averaging of symmetric diffusion in a random medium, Sibirsk. Mat. Zh. 27 (1986) 167–180. MR867870
  • N. G. Meyers, An LpL^pLp estimate for the gradient of solutions of second order elliptic divergence equations, Ann. Scuola Norm. Sup. Pisa 17 (1963) 189–206. MR0159110
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CombinatoricsGraph TheoryStatistics·Captain: mikedeng1

Random Graphs with a Given Degree Sequence 2: The Closure of the β-Model Expected Degree Sequences Equals the Convex Hull of All Degree SequencesResearch Paper

Motivation

A degree sequence records how many neighbours each vertex of a graph has. In network statistics it is often the only summary of a graph that is observed or trusted, and the natural probability models for graphs in which "the degree sequence captures the information" are exponential families whose sufficient statistic is the degree sequence. Chatterjee, Diaconis and Sly (arXiv:1005.1136, Ann. Appl. Probab. 2011) call the resulting model the β-model and study its maximum likelihood theory and its use for sampling random graphs with a prescribed degree sequence.

For any exponential family, the first question about estimation is which values of the sufficient statistic can be matched by a parameter: the maximum likelihood equations say exactly "the expected statistic equals the observed one". Theorem 1.4 of the paper answers this for the β-model. Up to taking a closure, the expected degree sequences of the β-model fill the whole convex hull of the degree sequences. The paper notes that the result can also be derived from classical results on the mean space of exponential families (Barndorff-Nielsen 1978; Brown 1986; Wainwright and Jordan 2008, Theorem 3.3), and gives a self-contained proof in §3. Lemma 4.1 of the same paper uses Theorem 1.4 to show that the maximum likelihood estimator exists.

Setting

Fix n≥0n\ge 0n≥0 and the vertex set {1,…,n}\{1,\dots,n\}{1,…,n}. A graph GGG is undirected and simple: no loops and no multiple edges. Its degree sequence is d(G)=(d1,…,dn)∈Rnd(G)=(d_1,\dots,d_n)\in\mathbb R^nd(G)=(d1​,…,dn​)∈Rn, and

D={d(G): G a simple graph on n vertices}\mathcal D=\{d(G):\ G\text{ a simple graph on }n\text{ vertices}\}D={d(G): G a simple graph on n vertices}

is the finite set of all degree sequences. Its convex hull conv⁡(D)\operatorname{conv}(\mathcal D)conv(D) is a polytope in Rn\mathbb R^nRn.

For β∈Rn\beta\in\mathbb R^nβ∈Rn, the β-model Pβ\mathbb P_\betaPβ​ is the law of the random graph in which each pair {i,j}\{i,j\}{i,j}, i≠ji\neq ji=j, is an edge with probability

pij=eβi+βj1+eβi+βj,p_{ij}=\frac{e^{\beta_i+\beta_j}}{1+e^{\beta_i+\beta_j}},pij​=1+eβi​+βj​eβi​+βj​​,

independently of all other pairs. The expected degree sequences form the set

R={(Eβ[d1],…,Eβ[dn]): β∈Rn}.\mathcal R=\big\{\big(\mathbb E_\beta[d_1],\dots,\mathbb E_\beta[d_n]\big):\ \beta\in\mathbb R^n\big\}.R={(Eβ​[d1​],…,Eβ​[dn​]): β∈Rn}.

Two functions on Rn\mathbb R^nRn appear in the proof. The first is g=(g1,…,gn)g=(g_1,\dots,g_n)g=(g1​,…,gn​) with

gi(x)=∑j≠iexi+xj1+exi+xj.g_i(x)=\sum_{j\neq i}\frac{e^{x_i+x_j}}{1+e^{x_i+x_j}}.gi​(x)=j=i∑​1+exi​+xj​exi​+xj​​.

The second is, for each y∈Rny\in\mathbb R^ny∈Rn,

fy(x)=∑i=1nxiyi−log⁡∏1≤i<j≤n(1+exi+xj).f_y(x)=\sum_{i=1}^n x_iy_i-\log\prod_{1\le i<j\le n}\big(1+e^{x_i+x_j}\big).fy​(x)=i=1∑n​xi​yi​−log1≤i<j≤n∏​(1+exi​+xj​).

The Lean development names these objects betaModel, D, R, g and fy in the namespace GivenDegreeSeq.MeanPolytope.

Formalization targets

Goal: Theorem 1.4 (p. 8)

conv⁡(D)=R‾for every n.\operatorname{conv}(\mathcal D)=\overline{\mathcal R}\qquad\text{for every }n.conv(D)=Rfor every n.

The statement fixes no constants. The closure cannot be dropped: for n≥2n\ge2n≥2 the degree sequence 000 of the empty graph lies in D\mathcal DD but not in R\mathcal RR, because every expected degree is positive.

Milestones, in the order the proof uses them

  1. The probability formula (p. 6): Pβ\mathbb P_\betaPβ​ is a probability measure, and
Pβ({G})=e∑iβidi∏i<j(1+eβi+βj).\mathbb P_\beta(\{G\})=\frac{e^{\sum_i\beta_id_i}}{\prod_{i<j}(1+e^{\beta_i+\beta_j})}.Pβ​({G})=∏i<j​(1+eβi​+βj​)e∑i​βi​di​​.
  1. Expected degrees (p. 15): Ex[di]=gi(x)\mathbb E_x[d_i]=g_i(x)Ex​[di​]=gi​(x), so R=g(Rn)\mathcal R=g(\mathbb R^n)R=g(Rn).
  2. The easy inclusion (p. 15): R‾⊆conv⁡(D)\overline{\mathcal R}\subseteq\operatorname{conv}(\mathcal D)R⊆conv(D).
  3. A uniform bound (p. 16): fy(x)≤0f_y(x)\le 0fy​(x)≤0 for all y∈conv⁡(D)y\in\operatorname{conv}(\mathcal D)y∈conv(D) and x∈Rnx\in\mathbb R^nx∈Rn.
  4. Smoothness (p. 16): fyf_yfy​ is twice differentiable and ∇2fy\nabla^2 f_y∇2fy​ is uniformly bounded.
  5. Lemma 3.1 (pp. 14–15): if fff is twice differentiable, M=sup⁡f<∞M=\sup f<\inftyM=supf<∞ and ∥∇2f∥op≤C\|\nabla^2 f\|_{\mathrm{op}}\le C∥∇2f∥op​≤C, then
∣∇f(x)∣2≤2C(M−f(x)),|\nabla f(x)|^2\le 2C\big(M-f(x)\big),∣∇f(x)∣2≤2C(M−f(x)),

and ∇f(xk)→0\nabla f(x_k)\to0∇f(xk​)→0 along some sequence. 7. The gradient (p. 16): ∇fy(x)=y−g(x)\nabla f_y(x)=y-g(x)∇fy​(x)=y−g(x).

Significance

The theorem describes the mean space of the β-model. Its interior contains every degree sequence that lies strictly inside the polytope, and for these sequences maximum likelihood estimation is well posed. The paper's consistency result (Theorem 1.3) and the existence part of Lemma 4.1 start from this description. The polytope conv⁡(D)\operatorname{conv}(\mathcal D)conv(D) is studied in its own right; its extreme points are the degree sequences of threshold graphs (Mahadev and Peled 1995).

Theorem 1.4 is proved, and as far as this mission's authors know it is not machine-checked anywhere. Formalizing it means turning the paper's short proof into a complete argument. That includes the steps the page dismisses as easy: the exponential-family formula for the independent-edge law, the expectation computation, the Hessian bound for fyf_yfy​, and the passage from a sequence of approximate critical points to the closure. Lemma 3.1, a descent-type gradient bound for a function bounded above, is a general fact of real analysis and is independent of graphs.

Difficulty

The inclusion R‾⊆conv⁡(D)\overline{\mathcal R}\subseteq\operatorname{conv}(\mathcal D)R⊆conv(D) is direct: an expectation is an average. The reverse inclusion is the content. For yyy in the interior of the polytope, one would like to find xxx with g(x)=yg(x)=yg(x)=y by maximizing the concave function fyf_yfy​. For yyy on the boundary of the polytope, however, fyf_yfy​ need not attain its supremum. So the argument cannot rely on the existence of a maximizer, and it has to produce points xkx_kxk​ with g(xk)→yg(x_k)\to yg(xk​)→y without ever solving g(x)=yg(x)=yg(x)=y. Compactness arguments on β\betaβ fail for the same reason: the relevant sequences xkx_kxk​ are unbounded.

Formalization scope

  • Graphs and measure. Vertices are Fin n, so the paper's vertex iii is the index i−1i-1i−1. Graphs are SimpleGraph (Fin n), a finite type. Its σ-algebra is Mathlib's SimpleGraph.instMeasurableSpace, under which every set of graphs on Fin n is measurable.
  • The law and its expectations. Pβ\mathbb P_\betaPβ​ is the finite sum of Dirac masses weighted by the independent-edge product over pairs i<ji<ji<j. R\mathcal RR is defined through Bochner expectations under Pβ\mathbb P_\betaPβ​, not as the range of ggg. Defining R\mathcal RR as g(Rn)g(\mathbb R^n)g(Rn) would erase the link to the probability model and is ruled out.
  • Vector spaces. D\mathcal DD and R\mathcal RR are subsets of Fin n → ℝ, whose product topology is the Euclidean topology, so the closure is unambiguous. Gradients and Hessians are taken on EuclideanSpace ℝ (Fin n). There "twice differentiable" means that fff and its Fréchet derivative are differentiable, and the Hessian norm is the norm of the second Fréchet derivative, which equals the L2L^2L2 operator norm of the Hessian matrix.
  • No size hypothesis. The goal holds for every nnn; for n≤1n\le1n≤1 both sides are {0}\{0\}{0}.
  • Readings of the page.
    • On p. 15 the paper prints log⁡∑1≤i<j≤n(1+exi+xj)\log\sum_{1\le i<j\le n}(1+e^{x_i+x_j})log∑1≤i<j≤n​(1+exi​+xj​) in fyf_yfy​. The formalization uses log⁡∏\log\prodlog∏, the only reading under which "taking logs, we get fd(x)≤0f_d(x)\le0fd​(x)≤0" and ∇fy=y−g\nabla f_y=y-g∇fy​=y−g hold. The milestone text keeps the printed version.
    • The "Lemma 11" of p. 16 is Lemma 3.1.
    • The probability-measure property of Pβ\mathbb P_\betaPβ​ is stated as a conjunct of milestone 1.
    • The differentiability of fyf_yfy​ is stated alongside the Hessian bound in milestone 5.

Contributions of any milestone are welcome. Lemma 3.1 and the gradient identity are self-contained calculus. The probability formula and the expected-degree identity are finite computations on graphs.

Selected references

  • S. Chatterjee, P. Diaconis, A. Sly, Random graphs with a given degree sequence, Ann. Appl. Probab. 21(4) (2011), 1400–1435. https://arxiv.org/abs/1005.1136 (v5), https://doi.org/10.1214/10-AAP728
  • O. Barndorff-Nielsen, Information and Exponential Families in Statistical Theory, Wiley, Chichester, 1978. MR0489333, https://mathscinet.ams.org/mathscinet-getitem?mr=0489333
  • L. D. Brown, Fundamentals of Statistical Exponential Families with Applications in Statistical Decision Theory, IMS Lecture Notes–Monograph Series 9, 1986. MR0882001, https://mathscinet.ams.org/mathscinet-getitem?mr=0882001
  • M. J. Wainwright, M. I. Jordan, Graphical models, exponential families, and variational inference, Found. Trends Mach. Learn. 1 (2008), 1–305. https://doi.org/10.1561/2200000001
  • N. V. R. Mahadev, U. N. Peled, Threshold Graphs and Related Topics, Ann. Discrete Math. 56, North-Holland, 1995. MR1417258, https://mathscinet.ams.org/mathscinet-getitem?mr=1417258
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Operations ResearchOptimizationProbability·Captain: mikedeng1

Mitigating Supply Risk: Dual Sourcing or Process Improvement? 4: The Advantage of Single Sourcing with Improvement over Dual Sourcing Increases in Supplier Cost HeterogeneityResearch Paper

Motivation

Firms that buy from unreliable suppliers have two broad ways to protect themselves against supply disruptions: dual sourcing, which splits the order between two suppliers so that a shortfall at one is cushioned by the other, and process improvement, which invests in a supplier's operations so that it fails less often. Wang, Gilland and Tomlin, Mitigating Supply Risk: Dual Sourcing or Process Improvement? (M&SOM 12(3):489–510, 2010), compare the two in a single-period model in which the uncertainty is in the supplier's capacity, not in a proportional yield.

Real supply bases are rarely symmetric. Suppliers differ in cost, reliability and capacity because of their history or location, and the paper (p. 501) cites evidence that global sourcing has widened those differences. This mission formalizes the paper's analytical answer to one question this raises: as two otherwise identical suppliers drift apart in unit cost, which strategy gains? The paper's answer (Theorem 7, p. 502) is that the advantage of single sourcing with improvement over dual sourcing grows with the cost gap.

Setting

A firm sells one product over one season with unit revenue rrr, salvage value vvv and penalty ppp per unit of unmet demand; demand X≥0X \ge 0X≥0 has a known law with finite mean. Supplier i∈{1,2}i \in \{1, 2\}i∈{1,2} has unit cost cic_ici​, committed-cost fraction ηi∈[0,1]\eta_i \in [0, 1]ηi​∈[0,1] and design capacity Ki>0K_i > 0Ki​>0. Its realized capacity loss ξi≥0\xi_i \ge 0ξi​≥0 has a continuous distribution Gi(⋅,ai)G_i(\cdot, a_i)Gi​(⋅,ai​) indexed by a reliability index aia_iai​; a larger index means a stochastically smaller loss: a≤a′a \le a'a≤a′ implies Gi(t,a)≤Gi(t,a′)G_i(t, a) \le G_i(t, a')Gi​(t,a)≤Gi​(t,a′) for all ttt. Losses are independent of each other and of demand.

An order qi≥0q_i \ge 0qi​≥0 delivers yi=min⁡{qi,(Ki−ξi)+}y_i = \min\{q_i, (K_i - \xi_i)^+\}yi​=min{qi​,(Ki​−ξi​)+} and costs (ηiqi+(1−ηi)yi)ci(\eta_i q_i + (1 - \eta_i) y_i) c_i(ηi​qi​+(1−ηi​)yi​)ci​. The realized profit is

π(q)=−∑i(ηiqi+(1−ηi)yi)ci+rmin⁡{x,∑iyi}+v(∑iyi−x)+−p(x−∑iyi)+,\pi(q) = -\sum_i (\eta_i q_i + (1-\eta_i) y_i) c_i + r\min\Big\{x, \sum_i y_i\Big\} + v\Big(\sum_i y_i - x\Big)^+ - p\Big(x - \sum_i y_i\Big)^+,π(q)=−i∑​(ηi​qi​+(1−ηi​)yi​)ci​+rmin{x,i∑​yi​}+v(i∑​yi​−x)+−p(x−i∑​yi​)+,

the second-stage expected profit is Π2(q;a)=E[π(q)]\Pi_2(q; a) = \mathbb E[\pi(q)]Π2​(q;a)=E[π(q)], and Π2∗(a)=sup⁡q≥0Π2(q;a)\Pi_2^*(a) = \sup_{q \ge 0}\Pi_2(q; a)Π2∗​(a)=supq≥0​Π2​(q;a).

  • Dual sourcing (DS) orders from both suppliers at their initial indices: ΠDS∗=Π2∗(a10,a20)\Pi^*_{DS} = \Pi_2^*(a_1^0, a_2^0)ΠDS∗​=Π2∗​(a10​,a20​).
  • Single sourcing with improvement (SSI) commits to one supplier iii, spends mizi(a)m_i z_i(a)mi​zi​(a) to try to raise its index to a≥ai0a \ge a_i^0a≥ai0​ (success probability θi\theta_iθi​), and orders only from it. With Π2∗(ai)\Pi_2^*(a_i)Π2∗​(ai​) the single-supplier optimal value, its profit is Π1(ai)=−mizi(ai)+θiΠ2∗(ai)+(1−θi)Π2∗(ai0)\Pi_1(a_i) = -m_i z_i(a_i) + \theta_i \Pi_2^*(a_i) + (1-\theta_i)\Pi_2^*(a_i^0)Π1​(ai​)=−mi​zi​(ai​)+θi​Π2∗​(ai​)+(1−θi​)Π2∗​(ai0​) (Eq. (7)), and ΠSSI∗=max⁡isup⁡a≥ai0Π1(a)\Pi^*_{SSI} = \max_i \sup_{a \ge a_i^0} \Pi_1(a)ΠSSI∗​=maxi​supa≥ai0​​Π1​(a).
  • Heterogeneity. For suppliers identical except in cost, c1=c−Δcc_1 = c - \Delta_cc1​=c−Δc​ and c2=c+Δcc_2 = c + \Delta_cc2​=c+Δc​ with 0≤Δc<c0 \le \Delta_c < c0≤Δc​<c; Δc\Delta_cΔc​ is the cost heterogeneity parameter. The committed-cost parameter Δη\Delta_\etaΔη​ is defined in the same way.

In Lean these objects are MitigateSupplyRisk.Heterogeneity.Model (with Pi2, Pi2star, PiDS, Pi1single, PiSSI, Pi1) and SymData (with costModel Δ and etaModel Δ).

Formalization targets

Goal: Theorem 7 (p. 502)

For suppliers identical except in unit cost,

Δc↦ΠSSI∗(Δc)−ΠDS∗(Δc)is nondecreasing on [0,c).\Delta_c \mapsto \Pi^*_{SSI}(\Delta_c) - \Pi^*_{DS}(\Delta_c) \quad\text{is nondecreasing on } [0, c).Δc​↦ΠSSI∗​(Δc​)−ΠDS∗​(Δc​)is nondecreasing on [0,c).

The statement fixes no parameter values and no particular distribution; it asserts only the direction of the effect.

Milestones

  1. Theorem 6(a) (p. 501): ΠSSI∗\Pi^*_{SSI}ΠSSI∗​ is nondecreasing in Δc\Delta_cΔc​ on [0,c)[0, c)[0,c) and in Δη\Delta_\etaΔη​ on [0,min⁡{η,1−η}][0, \min\{\eta, 1-\eta\}][0,min{η,1−η}].
  2. Theorem 6(b) (p. 501): ΠDS∗\Pi^*_{DS}ΠDS∗​ is nondecreasing in Δc\Delta_cΔc​ and in Δη\Delta_\etaΔη​ on the same ranges.
  3. Lemma 5 (p. 504): the combined-strategy profit Π1(a)\Pi_1(a)Π1​(a) of Eq. (5), which improves one or both suppliers before dual sourcing, is submodular on [a10,∞)×[a20,∞)[a_1^0, \infty) \times [a_2^0, \infty)[a10​,∞)×[a20​,∞):
Π1(a∨b)+Π1(a∧b)≤Π1(a)+Π1(b).\Pi_1(a \vee b) + \Pi_1(a \wedge b) \le \Pi_1(a) + \Pi_1(b).Π1​(a∨b)+Π1​(a∧b)≤Π1​(a)+Π1​(b).

Significance

Theorem 7 turns a strategy comparison into a comparative-statics statement: once SSI is preferred at some cost gap, it stays preferred at every larger gap, so the preference switches at most once along a cost-heterogeneity path. Theorem 6 shows separately that both strategies benefit from heterogeneity, which is immediate for a single-sourcing strategy but not for dual sourcing, where one supplier improves and the other deteriorates. Lemma 5 is the structural fact the paper uses for the combined strategy: improvement efforts at the two suppliers are substitutes.

The results are proved in the paper's online appendix, which this formalization does not use. To our knowledge none of them has a machine-checked proof. A formal development would supply reusable pieces: an expected-profit model for random capacity with integrable profits, envelope and convexity arguments for suprema of affine families, and monotone comparative statics for single- and dual-supplier newsvendor problems.

Difficulty

ΠSSI∗\Pi^*_{SSI}ΠSSI∗​ and ΠDS∗\Pi^*_{DS}ΠDS∗​ are both nondecreasing in Δc\Delta_cΔc​ (Theorem 6), so the goal compares the growth rates of two optimal values. Neither has a closed form. Both are suprema of families affine in Δc\Delta_cΔc​, so they are convex but may have kinks, and an optimal improvement level need not exist when the index set [a0,∞)[a^0, \infty)[a0,∞) is unbounded. The comparison concerns orders at different cost levels and under different strategies, so it needs a link between the dual-sourcing order from the cheaper supplier and the single-sourcing order and its response to improvement. Evaluating each side at a fixed optimizer does not give it, because the two optimizers move with Δc\Delta_cΔc​. For Lemma 5, submodularity of Π2(q;a)\Pi_2(q; a)Π2​(q;a) in aaa for each fixed qqq does not pass to the supremum over qqq by itself.

Formalization scope

  • Representation. Suppliers are indexed by Fin 2. Each Gi(⋅,a)G_i(\cdot, a)Gi​(⋅,a) is the cdf of a measure ν i a on ℝ. Expectations are Bochner integrals against the product of the two loss laws and the demand law. The integrand is bounded by a constant times 1+∣x∣1 + |x|1+∣x∣, so the integrals are genuine.
  • Optimal values. Optimal values are real suprema over q≥0q \ge 0q≥0 and a≥ai0a \ge a_i^0a≥ai0​. Under the standing assumptions every such set is nonempty and bounded above by (r+∣v∣)(K1+K2)(r + |v|)(K_1 + K_2)(r+∣v∣)(K1​+K2​), so no supremum takes Lean's default value. ΠSSI∗\Pi^*_{SSI}ΠSSI∗​ is the early-commitment value and maximizes over the choice of supplier; it does not fix supplier 1.
  • Standing assumptions (fields of Model.Standing and SymData.Standing):
    • the demand is a probability law on [0,∞)[0, \infty)[0,∞) with finite mean;
    • each loss law is a continuous probability law on [0,∞)[0, \infty)[0,∞), stochastically decreasing in the index;
    • ηi∈[0,1]\eta_i \in [0, 1]ηi​∈[0,1], Ki>0K_i > 0Ki​>0, θi∈[0,1]\theta_i \in [0, 1]θi​∈[0,1], mi≥0m_i \ge 0mi​≥0;
    • ziz_izi​ is convex and nondecreasing on [ai0,∞)[a_i^0, \infty)[ai0​,∞) with zi(ai0)=0z_i(a_i^0) = 0zi​(ai0​)=0.
  • Disclosed readings.
    • r≥0r \ge 0r≥0, p≥0p \ge 0p≥0 and ci≥0c_i \ge 0ci​≥0 (c>0c > 0c>0 in the heterogeneity statements, so that c1>0c_1 > 0c1​>0) are the paper's readings of revenue, penalty and cost.
    • v<r+pv < r + pv<r+p is implicit in the paper's Eq. (2), which divides by r+p−vr + p - vr+p−v.
    • The effort function zi(a)z_i(a)zi​(a) is taken as the primitive, as on p. 493. This presumes that every index a≥ai0a \ge a_i^0a≥ai0​ is reachable.
    • Δη\Delta_\etaΔη​ ranges over [0,min⁡{η,1−η}][0, \min\{\eta, 1-\eta\}][0,min{η,1−η}], the reading of "analogously" (p. 501) that keeps both committed costs in [0,1][0, 1][0,1].
  • Weak monotonicity. "Increasing" is weak (p. 492) and is stated as MonotoneOn.
  • Corrections. No printed slip is corrected in this mission.
  • Not formalized. Theorem 7 is stated without η=0\eta = 0η=0 and without concavity of GGG in aaa, as on the page. The ΔK\Delta_KΔK​ and Δa\Delta_aΔa​ clauses of Theorem 6 are not formalized, because the paper does not pin down how the improvement function depends on an initial index that differs between suppliers. Theorem 8 is also left out.
  • Non-trivialization. Both values are recomputed as optimal values of the model at every Δc\Delta_cΔc​. Defining them by a formula, fixing an optimizer at Δc=0\Delta_c = 0Δc​=0, or hard-coding supplier 1 as the single source would trivialize the goal, and none of these is done.

Contributions are welcome at every level: integrability and boundedness lemmas for Π2\Pi_2Π2​, convexity of the optimal values in Δ\DeltaΔ, the symmetry argument behind Theorem 6(b), and monotone comparative statics of single-supplier orders in the reliability index.

Selected references

  • Y. Wang, W. Gilland, B. Tomlin, Mitigating Supply Risk: Dual Sourcing or Process Improvement?, Manufacturing & Service Operations Management 12(3):489–510, 2010. https://doi.org/10.1287/msom.1090.0279
  • J. Hazra, B. Mahadevan, Impact of supply base heterogeneity in electronic markets, European Journal of Operational Research 174(3):1580–1594, 2006 (cited on p. 501 of the paper for the growth of supply-base heterogeneity).
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CombinatoricsGraph TheoryOperations Research·Captain: mikedeng1

On Metric Generators of Graphs 3: A Connected Minimum T-Join Exists Exactly When μ_G|T Is a Tree Metric Whose Minimal Realization Is a T′-Join Embedding Isometrically in GResearch Paper

Motivation

A TTT-join of a graph GGG is a set of edges FFF such that exactly the vertices of a prescribed even set TTT have odd degree in FFF. Minimum TTT-joins are a classical object of combinatorial optimization: they contain shortest paths (∣T∣=2|T|=2∣T∣=2), Chinese postman tours and perfect matchings as special cases, and their duality with TTT-cuts connects them to integral multiflows. Whether the minimum size τ(G,T)\tau(G,T)τ(G,T) of a TTT-join equals the maximum number ν(G,T)\nu(G,T)ν(G,T) of disjoint TTT-cuts has been studied extensively; equality always holds in bipartite graphs (Seymour 1981).

Sebő and Tannier (Math. Oper. Res. 2004) study the number of connected components of a minimum TTT-join, motivated by the observation that a minimum TTT-join with few components yields a large integral packing of TTT-cuts. Deciding whether some minimum TTT-join has at most kkk components is NP-complete in general (their Theorem 5). This mission formalizes the opposite end, k=1k=1k=1: Theorem 6 characterizes exactly when a minimum TTT-join can be chosen connected, in terms of the shortest-path metric of GGG restricted to TTT. The result first appeared in the authors' IPCO paper Connected joins in graphs (2001); the 2004 article derives it from its theory of isometric embeddings.

Setting

All graphs are finite, simple, undirected and connected. For vertices x,yx,yx,y of GGG, μG(x,y)\mu_G(x,y)μG​(x,y) is the number of edges of a shortest xxx–yyy path. For T⊆V(G)T\subseteq V(G)T⊆V(G), μG∣T\mu_G|_TμG​∣T​ is the restriction of μG\mu_GμG​ to pairs of vertices of TTT.

For an edge set F⊆E(G)F\subseteq E(G)F⊆E(G), deg⁡F(v)\deg_F(v)degF​(v) is the number of edges of FFF at vvv; FFF is a TTT-join if deg⁡F(v)\deg_F(v)degF​(v) is odd exactly when v∈Tv\in Tv∈T. A minimum TTT-join has the fewest edges among all TTT-joins; a minimal one has no proper subset that is a TTT-join. V(F)V(F)V(F) is the set of endpoints of edges of FFF; FFF is connected (a tree) when the graph (V(F),F)(V(F),F)(V(F),F) is connected (a tree), and μF\mu_FμF​ is the distance in (V(F),F)(V(F),F)(V(F),F).

An isometry from (X,μ)(X,\mu)(X,μ) to (Y,ν)(Y,\nu)(Y,ν) is a map fff with ν(f(x),f(y))=μ(x,y)\nu(f(x),f(y))=\mu(x,y)ν(f(x),f(y))=μ(x,y) for all x,yx,yx,y. A metric μ\muμ on a finite set XXX is a tree metric if there is a tree AAA (unit edge lengths) and an isometry ggg from (X,μ)(X,\mu)(X,μ) to AAA; the pair (A,g)(A,g)(A,g) is a realization. A realization is inclusionwise minimal if no proper subtree of AAA contains g(X)g(X)g(X). A tree AAA is an SSS-join if its vertices of odd degree are exactly those of SSS.

Formalization targets

Goal: Theorem 6

For GGG connected and TTT nonempty of even cardinality,

∃ F minimum connected T-join  ⟺  {μG∣T is a tree metric, and for its minimal realization (A,g), T′:=g(T):A is a T′-join,∃ φ:V(A)→V(G) isometry with φ(g(t))=t (t∈T).\exists\,F\ \text{minimum connected }T\text{-join} \iff \begin{cases}\mu_G|_T \text{ is a tree metric, and for its minimal realization }(A,g),\ T':=g(T):\\ A \text{ is a } T'\text{-join},\\ \exists\,\varphi:V(A)\to V(G)\ \text{isometry with } \varphi(g(t))=t\ (t\in T).\end{cases}∃F minimum connected T-join⟺⎩⎨⎧​μG​∣T​ is a tree metric, and for its minimal realization (A,g), T′:=g(T):A is a T′-join,∃φ:V(A)→V(G) isometry with φ(g(t))=t (t∈T).​

The conditions are required of every minimal realization; all of them are isomorphic.

Milestones

  1. (p. 389) A minimal TTT-join is the edge-disjoint union of ∣T∣/2|T|/2∣T∣/2 paths pairing the vertices of TTT.
  2. (p. 391) In every realization of μ\muμ, the distance from g(x)g(x)g(x) to the g(y)g(y)g(y)–g(z)g(z)g(z) path equals 12(μ(x,y)+μ(x,z)−μ(y,z))\tfrac12(\mu(x,y)+\mu(x,z)-\mu(y,z))21​(μ(x,y)+μ(x,z)−μ(y,z)).
  3. (p. 391) A tree metric has an inclusionwise minimal realization, unique up to an isomorphism respecting the realization maps.
  4. (Lemma 2, p. 392) A connected TTT-join FFF is minimum if and only if FFF is a tree and μF=μG\mu_F=\mu_GμF​=μG​ on V(F)V(F)V(F).
  5. (p. 392) If AAA is a T′T'T′-join and φ\varphiφ an isometry from AAA to GGG extending g−1g^{-1}g−1, the image of AAA under φ\varphiφ is a TTT-join that is a tree with μF=μG\mu_F=\mu_GμF​=μG​ on V(F)V(F)V(F).

Significance

Theorem 6 turns the existence of a connected minimum TTT-join, a question about all TTT-joins of GGG, into conditions on the finite metric μG∣T\mu_G|_TμG​∣T​ alone plus one isometric embedding of a tree into GGG. Tree metrics can be recognized and their minimal realization constructed efficiently (Buneman 1974), and the embedding problem is the one solved in §2 of the paper, since T′T'T′ contains all leaves of AAA. The authors deduce that connected minimum TTT-joins can be found in polynomial time, which contrasts with the NP-completeness of the same question for kkk components. A consequence stated in the paper: if the minimal realization of μG∣T\mu_G|_TμG​∣T​ is not a T′T'T′-join, then no minimum TTT-join is connected.

The result is proved in the paper. No machine-checked version of Theorem 6, of Lemma 2, or of the uniqueness of minimal tree realizations is known to exist; Mathlib has graph distances, trees and walks, but neither TTT-joins nor tree metrics. The mission produces these definitions and a formal proof of the characterization; the complexity consequence is out of scope.

Difficulty

The central difficulty is in the necessity direction. A connected minimum TTT-join FFF is itself a minimal realization of μG∣T\mu_G|_TμG​∣T​ (Lemma 2), so FFF satisfies the conditions; but the conditions concern the minimal realization, and transferring them from FFF to an arbitrary minimal realization requires that minimal realizations are unique up to a label-preserving isomorphism. That uniqueness (milestone 3) is the step where the obvious approach stops: it is a statement about all trees realizing a metric, not about the graph GGG.

Formalization scope

  • Graphs are SimpleGraph V with [Fintype V] [DecidableEq V]; G.Connected is a hypothesis of every statement about GGG (SimpleGraph.dist is 000 on unreachable pairs). Distances are natural numbers (SimpleGraph.dist).
  • Edge sets are Finset (Sym2 V). Connectivity, the tree property and μF\mu_FμF​ refer to the graph with edge set FFF induced on V(F)V(F)V(F), not on all of VVV.
  • "Minimum" is "∣F∣≤∣F′∣|F|\le|F'|∣F∣≤∣F′∣ for every TTT-join F′F'F′", never an infimum over N\mathbb NN.
  • A realization is a tree on Fin N together with a map g:X→g:X\tog:X→ Fin N; this avoids quantifying over a universe. A tree metric must satisfy μ(x,y)=0⇒x=y\mu(x,y)=0\Rightarrow x=yμ(x,y)=0⇒x=y.
  • "The unique minimal realization" is encoded by quantifying over all inclusionwise minimal realizations. A goal that only asks for some minimal realization satisfying the conditions is a different, weaker statement (its necessity half needs no uniqueness) and is not this mission's target.
  • T≠∅T\neq\emptysetT=∅ is added: for T=∅T=\emptysetT=∅ the minimum TTT-join is empty and its connectivity is only a convention.
  • "fff is the inverse of ggg and φ\varphiφ extends fff" is encoded as φ(g(t))=t\varphi(g(t))=tφ(g(t))=t. Isometries preserve all distances, not only adjacency.
  • Halving and subtraction (milestone 2) are multiplied out.

Reusable beyond this mission: the TTT-join vocabulary, the path decomposition of minimal TTT-joins, and tree metrics with the uniqueness of minimal realizations. Contributions to any milestone are welcome, as are alternative proofs of milestone 3.

Selected references

  • András Sebő, Eric Tannier, On Metric Generators of Graphs, Mathematics of Operations Research 29(2):383–393, 2004. https://doi.org/10.1287/moor.1030.0070
  • András Sebő, Eric Tannier, Connected joins in graphs, Integer Programming and Combinatorial Optimization (IPCO 2001), Lecture Notes in Computer Science 2081, 383–395, 2001. https://doi.org/10.1007/3-540-45535-3
  • Peter Buneman, A note on the metric properties of trees, Journal of Combinatorial Theory, Series B 17:48–50, 1974. https://doi.org/10.1016/0095-8956(74)90047-1
  • Paul Seymour, On odd cuts and plane multicommodity flows, Proceedings of the London Mathematical Society 42:178–192, 1981. https://doi.org/10.1112/plms/s3-42.1.178
9 thms1 active userReviewed
Bandit AlgorithmsOperations ResearchProbability·Captain: mikedeng1

Linearly Parameterized Bandits 4: For Finitely Many Arms, the Uncertainty Ellipsoid Policy Has Regret at Most a₆|U|‖z‖ + a₇|U| Σᵤ min{log T/Δᵘ(z), TΔᵘ(z)}Research Paper

Motivation

In a linearly parameterized bandit, the expected rewards of many arms are driven by a small number of unknown parameters. Each arm is a vector u∈Rru \in \mathbb R^ru∈Rr, and its expected reward is the inner product u′Zu'Zu′Z with an unknown parameter vector ZZZ. Problems of this kind arise in marketing and revenue management, where each product is described by rrr features (price, popularity, …) and the expected revenues of thousands of products are, to a good approximation, linear in a few unknown feature weights. Pulling one arm then reveals information about all of them, and a good policy has to exploit this correlation.

Rusmevichientong and Tsitsiklis (arXiv:0812.3465v2, 2010) studied this model with unbounded, sub-Gaussian noise and a prior on ZZZ. They proved an Ω(rT)\Omega(r\sqrt T)Ω(rT​) lower bound, a matching policy for smooth arm sets, and the Uncertainty Ellipsoid (UE) policy for arbitrary arm sets. This mission formalizes their Theorem 4.2: when the set of arms is finite, the regret of UE grows like log⁡T\log TlogT, within a constant factor of the Lai–Robbins lower bound for the classical multi-armed bandit (Lai and Robbins, 1985).

Timeline.

  • 1985: Lai and Robbins prove that, for independent arms, the regret of any uniformly good policy grows at least like log⁡T\log TlogT, and give policies attaining it.
  • 2002: Auer, Cesa-Bianchi and Fischer give the finite-time UCB1 analysis (doi:10.1023/A:1013689704352), whose pull-count argument the UE analysis adapts. Auer, in the same year, studies linear payoffs with confidence bounds (JMLR 3).
  • 2010: Rusmevichientong and Tsitsiklis treat unbounded sub-Gaussian noise with an anytime policy (UE), and prove the log⁡T\log TlogT regret and log⁡2T\log^2 Tlog2T Bayes-risk bounds for finitely many arms formalized here.

Setting

Let r≥2r \ge 2r≥2 and let Ur⊂Rr\mathcal U_r \subset \mathbb R^rUr​⊂Rr be a finite, nonempty set of arms. Fix z∈Rrz \in \mathbb R^rz∈Rr, the value of the unknown parameter. Playing arm uuu in period ttt yields the reward Xt=u′z+WtX_t = u'z + W_tXt​=u′z+Wt​. The noise WtW_tWt​ is drawn afresh in each period from a law νu\nu_uνu​ that depends only on the arm played, independently of the past. A policy chooses the arm Ut+1U_{t+1}Ut+1​ of period t+1t+1t+1 as a function of the history Ht=(U1,X1,…,Ut,Xt)H_t = (U_1, X_1, \dots, U_t, X_t)Ht​=(U1​,X1​,…,Ut​,Xt​). The regret given Z=zZ = zZ=z is

Regret(z,T,ψ)=∑t=1TE[max⁡v∈Urv′z−Ut′z ∣ Z=z],\mathrm{Regret}(z, T, \psi) = \sum_{t=1}^T \mathbb E\Big[\max_{v \in \mathcal U_r} v'z - U_t'z \,\Big|\, Z = z\Big],Regret(z,T,ψ)=t=1∑T​E[v∈Ur​max​v′z−Ut′​z​Z=z],

and for a prior μ\muμ of ZZZ the Bayes risk is Risk(T,ψ)=EZ∼μ[Regret(Z,T,ψ)]\mathrm{Risk}(T, \psi) = \mathbb E_{Z \sim \mu}[\mathrm{Regret}(Z, T, \psi)]Risk(T,ψ)=EZ∼μ​[Regret(Z,T,ψ)]. The gap of arm uuu is Δu(z)=max⁡v∈Urv′z−u′z\Delta^u(z) = \max_{v \in \mathcal U_r} v'z - u'zΔu(z)=maxv∈Ur​​v′z−u′z, and Nu(z,T)N^u(z, T)Nu(z,T) is the number of periods among the first TTT in which uuu is played.

Assumption 1.

  • (a) Every νu\nu_uνu​ has mean zero and E[exW]≤ex2σ02/2\mathbb E[e^{xW}] \le e^{x^2\sigma_0^2/2}E[exW]≤ex2σ02​/2 for all xxx.
  • (b) Every arm has norm at most uˉ\bar uuˉ, and Ur\mathcal U_rUr​ contains rrr linearly independent arms b1,…,brb_1, \dots, b_rb1​,…,br​ with λmin⁡(∑kbkbk′)≥λ0\lambda_{\min}(\sum_k b_kb_k') \ge \lambda_0λmin​(∑k​bk​bk′​)≥λ0​.

The UE policy first plays b1,…,brb_1, \dots, b_rb1​,…,br​. It then forms the least squares estimate Z^t=Ct∑s≤tUsXs\widehat Z_t = C_t\sum_{s\le t}U_sX_sZt​=Ct​∑s≤t​Us​Xs​ with Ct=(∑s≤tUsUs′)−1C_t = (\sum_{s \le t} U_sU_s')^{-1}Ct​=(∑s≤t​Us​Us′​)−1, and plays an arm maximizing v′Z^t+Rtvv'\widehat Z_t + R^v_tv′Zt​+Rtv​. The uncertainty radius is

Rtv=αlog⁡tmin⁡{rlog⁡t,∣Ur∣} v′Ctv,R^v_t = \alpha\sqrt{\log t}\sqrt{\min\{r\log t, |\mathcal U_r|\}}\,\sqrt{v'C_tv},Rtv​=αlogt​min{rlogt,∣Ur​∣}​v′Ct​v​,

with α=4σ0κ02\alpha = 4\sigma_0\kappa_0^2α=4σ0​κ02​ and κ0=21+log⁡(1+36uˉ2/λ0)\kappa_0 = 2\sqrt{1 + \log(1 + 36\bar u^2/\lambda_0)}κ0​=21+log(1+36uˉ2/λ0​)​. Ties are broken arbitrarily.

Formalization targets

Goal: Theorem 4.2

There are constants a6,a7>0a_6, a_7 > 0a6​,a7​>0 depending only on σ0,uˉ,λ0\sigma_0, \bar u, \lambda_0σ0​,uˉ,λ0​ such that for all T≥r+1T \ge r+1T≥r+1 and zzz,

Regret(z,T,UE)≤a6∣Ur∣ ∥z∥+a7∣Ur∣∑u∈Urmin⁡{log⁡TΔu(z),TΔu(z)}.\mathrm{Regret}(z, T, \mathrm{UE}) \le a_6|\mathcal U_r|\,\|z\| + a_7|\mathcal U_r|\sum_{u \in \mathcal U_r}\min\Big\{\frac{\log T}{\Delta^u(z)}, T\Delta^u(z)\Big\}.Regret(z,T,UE)≤a6​∣Ur​∣∥z∥+a7​∣Ur​∣u∈Ur​∑​min{Δu(z)logT​,TΔu(z)}.

If moreover each Δu(Z)\Delta^u(Z)Δu(Z) has a point mass at 000 and a density bounded by M0M_0M0​ on R+\mathbb R_+R+​, there are a8,a9>0a_8, a_9 > 0a8​,a9​>0 depending only on σ0,uˉ,λ0,M0\sigma_0, \bar u, \lambda_0, M_0σ0​,uˉ,λ0​,M0​ with

Risk(T,UE)≤a8∣Ur∣ E∥Z∥+a9∣Ur∣2log⁡2T.\mathrm{Risk}(T, \mathrm{UE}) \le a_8|\mathcal U_r|\,\mathbb E\|Z\| + a_9|\mathcal U_r|^2\log^2 T.Risk(T,UE)≤a8​∣Ur​∣E∥Z∥+a9​∣Ur​∣2log2T.

The constants are left unspecified, as in the paper. Their existence, uniformly in rrr and in the arm set, is the content.

Milestones

  • Theorem B.1 and Theorem B.2: Chernoff-type deviation bounds for adaptive least squares, with factors t5∣Ur∣t^{5|\mathcal U_r|}t5∣Ur​∣ and trκ02t^{r\kappa_0^2}trκ02​.
  • Lemma B.6: the radius RtuR^u_tRtu​ is exceeded with probability at most 1/t21/t^21/t2.
  • The pull-count bound of App. B.3: E[Nu(z,T)]≤6+4α2∣Ur∣log⁡T/Δu(z)2\mathbb E[N^u(z,T)] \le 6 + 4\alpha^2|\mathcal U_r|\log T/\Delta^u(z)^2E[Nu(z,T)]≤6+4α2∣Ur​∣logT/Δu(z)2 for every suboptimal arm.
  • The regret decomposition Regret=∑uΔu(z) E[Nu(z,T)]\mathrm{Regret} = \sum_u\Delta^u(z)\,\mathbb E[N^u(z,T)]Regret=∑u​Δu(z)E[Nu(z,T)].
  • The risk bound E[min⁡{log⁡T/Δu(Z),TΔu(Z)}]≤(M0+1)log⁡T+M0log⁡2T\mathbb E[\min\{\log T/\Delta^u(Z), T\Delta^u(Z)\}] \le (M_0+1)\log T + M_0\log^2 TE[min{logT/Δu(Z),TΔu(Z)}]≤(M0​+1)logT+M0​log2T.

Significance

The result. For a fixed finite arm set, Theorem 4.2 shows that a single anytime policy, which does not know TTT, has regret O(log⁡T)O(\log T)O(logT) for every parameter and Bayes risk O(log⁡2T)O(\log^2 T)O(log2T). It does this with unbounded noise and correlated arms. The dependence on the problem enters only through the gaps Δu(z)\Delta^u(z)Δu(z) and the number of arms. The companion result for general compact arm sets (Theorem 4.1) gives only O~(rT)\tilde O(r\sqrt T)O~(rT​). The finite case shows that the same policy adapts to the easier problem.

Formalizing it. The theorem is proved on paper. As far as is known, it has no machine-checked proof. The platform's finite-armed bandit library (Lattimore–Szepesvári) has the regret decomposition and UCB pull-count bounds for independent arms, e.g. BanditAlgorithm.bandit_regret_decomposition. Those statements live in a different model and do not apply to correlated linear rewards with a least squares estimator. This mission adds:

  • self-normalized deviation bounds for adaptively collected least squares estimates, under per-arm sub-Gaussian noise;
  • a pull-count analysis that runs through a matrix-valued confidence radius;
  • the Bayes-risk integration under a density condition.

Difficulty

The arms are chosen adaptively, so the design matrix ∑sUsUs′\sum_s U_sU_s'∑s​Us​Us′​ is random and depends on the noise. Applied with the realized CtC_tCt​, the classical Chernoff bound for a fixed weighted sum of independent noises is not valid. The obvious union bound over arms does not apply either: the event concerns the random matrix CtC_tCt​, not one arm. In the pull-count bound, the radius of arm uuu must be controlled through the number of times uuu was played, although CtC_tCt​ mixes all arms, and the Gram matrix of the other arms may be singular.

Formalization scope

  • Space. Rr\mathbb R^rRr is EuclideanSpace ℝ (Fin r), so ∥⋅∥\|\cdot\|∥⋅∥ is Euclidean. The source is arXiv:0812.3465v2; its printed page numbers equal the PDF's.
  • Model.
    • The arm set is a finite nonempty Set, and ∣Ur∣|\mathcal U_r|∣Ur​∣ is its ncard. Theorem B.2 and Lemma B.6, which the paper states for any compact arm set, are stated for compact nonempty arm sets, as printed.
    • The noise is a Markov kernel u↦νuu \mapsto \nu_uu↦νu​.
    • The law of the history given Z=zZ = zZ=z is built period by period, with fresh noise from νUt+1\nu_{U_{t+1}}νUt+1​​. This is the paper's model: noises independent of each other and of ZZZ, identically distributed in ttt, mean zero.
    • Policies are deterministic and history-dependent, with measurable selection rules. The paper uses this measurability implicitly.
    • Regret is Tmax⁡vv′z−E[∑tUt′z]T\max_v v'z - \mathbb E[\sum_t U_t'z]Tmaxv​v′z−E[∑t​Ut′​z].
    • Assumption 1(a) is an mgf bound written with a lower integral, so the exponential moments are finite. Assumption 1(b) writes λmin⁡≥λ0\lambda_{\min} \ge \lambda_0λmin​≥λ0​ as λ0∥x∥2≤∑k(bk′x)2\lambda_0\|x\|^2 \le \sum_k (b_k'x)^2λ0​∥x∥2≤∑k​(bk′​x)2.
    • A UE run is any measurable policy that plays b1,…,brb_1, \dots, b_rb1​,…,br​ first and then an arg max of (7). The theorems hold for every tie-breaking rule.
  • Constants. They are quantified before rrr, the arm set, the noise, the policy, TTT, zzz and the prior, so they cannot depend on any of these.
  • Corrections and added hypotheses.
    • The pull-count bound is stated for arms with Δu(z)>0\Delta^u(z) > 0Δu(z)>0. As printed, it divides by a zero gap for an optimal arm.
    • The risk part assumes E∥Z∥<∞\mathbb E\|Z\| < \inftyE∥Z∥<∞ and concludes the integrability of the regret.
  • Conventions.
    • An optimal arm contributes min⁡{log⁡T/0,0}=0\min\{\log T/0, 0\} = 0min{logT/0,0}=0, the paper's reading and Lean's value.
    • The density condition says that, on (0,∞)(0,\infty)(0,∞), the law of Δu(Z)\Delta^u(Z)Δu(Z) is at most M0M_0M0​ times Lebesgue measure.
  • No trivializing reading. The regret is never a junk-valued integral that makes the bound free: the integrand is bounded and measurable, and a junk value would only raise the regret. The width min⁡{rlog⁡t,∣Ur∣}\min\{r\log t, |\mathcal U_r|\}min{rlogt,∣Ur​∣} uses the true cardinality, so the radius is not zero.
  • Contributions welcome. Reusable pieces are the history-measure construction, Chernoff bounds for adaptive designs, and the deviation bounds of Theorems B.1–B.2, which the companion mission on general compact arm sets also needs.

Selected references

  • P. Rusmevichientong, J. N. Tsitsiklis, Linearly Parameterized Bandits, arXiv:0812.3465v2, 24 Feb 2010. https://arxiv.org/abs/0812.3465
  • T. L. Lai, H. Robbins, Asymptotically efficient adaptive allocation rules, Advances in Applied Mathematics 6, 1985. https://doi.org/10.1016/0196-8858(85)90002-8
  • P. Auer, N. Cesa-Bianchi, P. Fischer, Finite-time analysis of the multiarmed bandit problem, Machine Learning 47, 2002. https://doi.org/10.1023/A:1013689704352
  • P. Auer, Using confidence bounds for exploitation–exploration trade-offs, JMLR 3, 2002. https://www.jmlr.org/papers/v3/auer02a.html
  • V. H. de la Peña, M. J. Klass, T. L. Lai, Self-normalized processes: exponential inequalities, moment bounds and iterated logarithm laws, Annals of Probability 32, 2004. https://doi.org/10.1214/009117904000000397
12 thms1 active userReviewed
Linear algebraProbabilityRandom Matrix Theory·Captain: mikedeng1

Random Matrices: Universality of ESDs and the Circular Law 2: The ESD of A_n/√n Converges to μ iff the Normalized Log-Determinants of A_n/√n − zI Converge to μ's Log-Potential for a.e. zResearch Paper

Motivation

The eigenvalues of a large non-Hermitian random matrix are hard to study directly. For Hermitian matrices, the empirical spectral distribution (ESD) is controlled by the resolvent and the Stieltjes transform, both stable under small perturbations. For non-Hermitian matrices no such tool exists: eigenvalues can move a long way under tiny perturbations, and the moment method fails because moments do not determine a measure on C\mathbb CC. The route that works, going back to Girko (1984) and used by Bai (1997), Götze–Tikhomirov (2007), Pan–Zhou (2007) and Tao–Vu (2008), passes through logarithmic determinants. The identity 1nlog⁡∣det⁡(A−zI)∣=∫log⁡∣w−z∣ dμA(w)\frac1n\log|\det(A-zI)|=\int\log|w-z|\,d\mu_A(w)n1​log∣det(A−zI)∣=∫log∣w−z∣dμA​(w) links the ESD μA\mu_AμA​ to a scalar quantity. That scalar is computable from the singular values of A−zIA-zIA−zI, which are eigenvalues of a Hermitian matrix.

Tao and Vu, with an appendix by Krishnapur (Ann. Probab. 38 (2010) 2023–2065), turn this link into an exact criterion. Theorem 1.15 of that paper states that the ESD of a normalized i.i.d. random matrix converges to a measure μ\muμ if and only if the normalized log-determinants converge to the logarithmic potential of μ\muμ for almost every zzz. The same holds for a regularized version built from the singular values. This mission formalizes Theorem 1.15 and the main steps of its proof (§8 and Appendix A of the published article).

Setting

Let xxx be a complex random variable with Ex=0\mathbf E x=0Ex=0 and E∣x∣2=1\mathbf E|x|^2=1E∣x∣2=1, and let (xij)i,j≥1(x_{ij})_{i,j\ge1}(xij​)i,j≥1​ be independent copies of xxx on a probability space (Ω,P)(\Omega,\mathbf P)(Ω,P). For each nnn, Xn=(xij)1≤i,j≤nX_n=(x_{ij})_{1\le i,j\le n}Xn​=(xij​)1≤i,j≤n​. Let MnM_nMn​ be deterministic n×nn\times nn×n complex matrices with

(1.3)sup⁡n1n2∥Mn∥22<∞,∥M∥22=∑i,j∣mij∣2,\text{(1.3)}\qquad\sup_n\frac1{n^2}\|M_n\|_2^2<\infty,\qquad\|M\|_2^2=\sum_{i,j}|m_{ij}|^2,(1.3)nsup​n21​∥Mn​∥22​<∞,∥M∥22​=i,j∑​∣mij​∣2,

and put An=Mn+XnA_n=M_n+X_nAn​=Mn​+Xn​.

For A∈Mn(C)A\in M_n(\mathbb C)A∈Mn​(C) with eigenvalues λ1,…,λn\lambda_1,\dots,\lambda_nλ1​,…,λn​, counted with algebraic multiplicity, the ESD is μA=1n∑iδλi\mu_A=\frac1n\sum_i\delta_{\lambda_i}μA​=n1​∑i​δλi​​. Its singular values are σ1(A)≥⋯≥σn(A)≥0\sigma_1(A)\ge\dots\ge\sigma_n(A)\ge0σ1​(A)≥⋯≥σn​(A)≥0. A sequence of random measures μn\mu_nμn​ converges in probability to a deterministic μ\muμ if, for every continuous compactly supported f:C→Rf:\mathbb C\to\mathbb Rf:C→R, ∫f dμn−∫f dμ\int f\,d\mu_n-\int f\,d\mu∫fdμn​−∫fdμ tends to 000 in probability. It converges almost surely if, with probability one, ∫f dμn→∫f dμ\int f\,d\mu_n\to\int f\,d\mu∫fdμn​→∫fdμ for all such fff at once. The logarithmic potential of a measure μ\muμ is z↦∫log⁡∣w−z∣ dμ(w)z\mapsto\int\log|w-z|\,d\mu(w)z↦∫log∣w−z∣dμ(w).

Hypothesis (1.4) states that, for almost every zzz, the ESDs of the deterministic matrices (1nMn−zI)(1nMn−zI)∗(\frac1{\sqrt n}M_n-zI)(\frac1{\sqrt n}M_n-zI)^*(n​1​Mn​−zI)(n​1​Mn​−zI)∗ converge to a limit.

In Lean everything lives in the namespace UnivESD.LogDet. The ESD is esd, the one-based singular value is sv, logPotential is the logarithmic potential, and ConvInProb/ConvAS/ESDConvInProb/ESDConvAS are the modes of convergence. IIDArray is the entry model, HSBound is (1.3), shiftA M xs z n ω is 1nAn−zI\frac1{\sqrt n}A_n-zIn​1​An​−zI, and Cond14 is (1.4).

Formalization targets

Goal: Theorem 1.15

Let μ\muμ be a probability measure on C\mathbb CC with ∫∣z∣2 dμ<∞\int|z|^2\,d\mu<\infty∫∣z∣2dμ<∞. The following are equivalent:

  1. μ1nAn→μ\mu_{\frac1{\sqrt n}A_n}\to\muμn​1​An​​→μ in probability;
  2. for a.e. zzz, 1nlog⁡∣det⁡(1nAn−zI)∣→∫Clog⁡∣w−z∣ dμ(w)\displaystyle\frac1n\log\Big|\det\big(\tfrac1{\sqrt n}A_n-zI\big)\Big|\to\int_{\mathbb C}\log|w-z|\,d\mu(w)n1​log​det(n​1​An​−zI)​→∫C​log∣w−z∣dμ(w) in probability;
  3. for a.e. zzz there are εn>0\varepsilon_n>0εn​>0, εn→0\varepsilon_n\to0εn​→0, with
1nlog⁡det⁡((1nAn−zI)(1nAn−zI)∗+εnI)→2∫Clog⁡∣w−z∣ dμ(w)\frac1n\log\det\Big(\big(\tfrac1{\sqrt n}A_n-zI\big)\big(\tfrac1{\sqrt n}A_n-zI\big)^*+\varepsilon_nI\Big)\to2\int_{\mathbb C}\log|w-z|\,d\mu(w)n1​logdet((n​1​An​−zI)(n​1​An​−zI)∗+εn​I)→2∫C​log∣w−z∣dμ(w)

in probability.

Under (1.4) the same equivalence holds with almost sure convergence throughout.

Milestones, in the order the proof uses them

  • (8.1): for a.e. zzz, w↦log⁡∣w−z∣w\mapsto\log|w-z|w↦log∣w−z∣ is μ\muμ-integrable.
  • Lemma A.3 (Weyl's comparison for products): ∏j≤J∣λj∣≤∏j≤Jσj(A)\prod_{j\le J}|\lambda_j|\le\prod_{j\le J}\sigma_j(A)∏j≤J​∣λj​∣≤∏j≤J​σj​(A) and ∏j≥Jσj(A)≤∏j≥J∣λj∣\prod_{j\ge J}\sigma_j(A)\le\prod_{j\ge J}|\lambda_j|∏j≥J​σj​(A)≤∏j≥J​∣λj​∣.
  • (8.3): P(σn−i≤ci/n)=O(exp⁡(−n0.01))\mathbf P(\sigma_{n-i}\le ci/n)=O(\exp(-n^{0.01}))P(σn−i​≤ci/n)=O(exp(−n0.01)) for 2n0.99≤i≤n−12n^{0.99}\le i\le n-12n0.99≤i≤n−1.
  • (8.2): 1n∑i=1n(log⁡σi2+εn−log⁡σi)→0\frac1n\sum_{i=1}^n\big(\log\sqrt{\sigma_i^2+\varepsilon_n}-\log\sigma_i\big)\to0n1​∑i=1n​(logσi2​+εn​​−logσi​)→0 almost surely.
  • p. 2057: almost surely, eventually, 1n∑(1−κ)n<i≤nlog⁡1σi≤O(κlog⁡1κ)\frac1n\sum_{(1-\kappa)n<i\le n}\log\frac1{\sigma_i}\le O(\kappa\log\frac1\kappa)n1​∑(1−κ)n<i≤n​logσi​1​≤O(κlogκ1​), and the same bound for log⁡1∣λi∣\log\frac1{|\lambda_i|}log∣λi​∣1​.

Significance

Theorem 1.15 reduces convergence of a non-Hermitian ESD to a family of scalar limit statements. Each of these can be attacked through Hermitian tools, because the regularized log-determinant in (iii) is a smooth functional of the singular values. Corollary 1.16 of the paper derives from it a practical criterion: the ESD converges to μ\muμ once the singular value distributions of 1nAn−zI\frac1{\sqrt n}A_n-zIn​1​An​−zI converge for a.e. zzz to laws ηz\eta_zηz​ with ∫log⁡t dηz=2∫log⁡∣w−z∣ dμ\int\log t\,d\eta_z=2\int\log|w-z|\,d\mu∫logtdηz​=2∫log∣w−z∣dμ. With the universality principle (Theorem 1.5, the companion mission) and Mehta's computation for complex Gaussian matrices, this yields the circular law for i.i.d. matrices with finite variance.

The theorem is proved in the source. To our knowledge neither it nor any of its milestones has a machine-checked proof. Mathlib has the ingredients for the objects (characteristic polynomials and their roots, LinearMap.singularValues, the strong law of large numbers) but not the results. A formal proof would give the platform its first verified bridge between eigenvalue and singular value asymptotics. Lemma A.3 alone, Weyl's multiplicative majorization, is a standard linear-algebra fact absent from Mathlib.

Difficulty

Two things make the obvious argument fail. First, log⁡\loglog is unbounded at 000. Vague convergence of μ1nAn\mu_{\frac1{\sqrt n}A_n}μn​1​An​​ controls ∫f dμn\int f\,d\mu_n∫fdμn​ only for continuous compactly supported fff, while log⁡∣w−z∣\log|w-z|log∣w−z∣ has a singularity at w=zw=zw=z and grows at infinity. Passing between (i) and (ii) therefore requires uniform control of the eigenvalues (and singular values) near 000 after the shift: the contribution of the smallest ones must vanish. Second, eigenvalues are not stable under perturbation, so this control cannot be read off the eigenvalues. It has to come from the singular values, through a lower-tail bound on σn−i\sigma_{n-i}σn−i​ that is exponentially small in a power of nnn, and through a deterministic inequality transferring bounds from singular values to eigenvalues. The smallest singular values (iii below n0.99n^{0.99}n0.99) need a separate polynomial lower bound (the paper's Lemma 4.1, posed in the companion mission). The tail bound in its printed form is false at that end of the range.

Formalization scope

Conventions committed to in Lean:

  • The ESD is n−1n^{-1}n−1 times the sum of Dirac masses over the root multiset of the characteristic polynomial, so multiplicities are kept. Test functions are real-valued, continuous and compactly supported. Almost sure ESD convergence uses one null set for all test functions.
  • The matrices XnX_nXn​ are the top-left corners of one infinite i.i.d. array. In-probability statements do not depend on how the XnX_nXn​ are coupled across nnn, but almost-sure statements do, and the paper's proof applies the strong law to this array. The limit in (1.4) is required to be a probability measure, which is equivalent under (1.3).
  • The second-moment condition on μ\muμ is Integrable (‖w‖²) μ. The logarithmic potential is a Bochner integral, which is 000 when not integrable; by (8.1) this happens only for a null set of zzz, which "for a.e. zzz" ignores.
  • Real.log 0 = 0. In (ii), a singular matrix would contribute 000 instead of −∞-\infty−∞; for this model the determinant is almost surely nonzero eventually. Every milestone with log⁡σi\log\sigma_ilogσi​ or log⁡∣λi∣\log|\lambda_i|log∣λi​∣ carries positivity of those quantities in its conclusion.
  • Singular values are one-based (sv A i is Mathlib's singularValues (i-1)). Eigenvalue enumerations are Fin n-indexed, and the paper's λi\lambda_iλi​ is index i−1i-1i−1.
  • The paper's O(⋅)O(\cdot)O(⋅) constants are existential and fixed before nnn. In the κlog⁡(1/κ)\kappa\log(1/\kappa)κlog(1/κ) bounds the constant is fixed before κ\kappaκ; a κ\kappaκ-dependent constant would make the bounds empty.

Disclosed corrections to the printed text:

  1. (iii)'s matrix. Theorem 1.15 prints a mis-bracketed product, and (1.8) prints (C+εnI)(C∗+εnI)(C+\varepsilon_nI)(C^*+\varepsilon_nI)(C+εn​I)(C∗+εn​I). The Lean uses CC∗+εnICC^*+\varepsilon_nICC∗+εn​I with C=1nAn−zIC=\frac1{\sqrt n}A_n-zIC=n​1​An​−zI, the matrix of (8.2) and of the proof of Corollary 1.16.
  2. (8.3)'s range. It is printed for 1≤i≤n−n0.991\le i\le n-n^{0.99}1≤i≤n−n0.99, which is false for small iii: for Gaussian entries and Mn=0M_n=0Mn​=0, nσn−1n\sigma_{n-1}nσn−1​ has a non-degenerate limit law. It is posed on 2n0.99≤i≤n−12n^{0.99}\le i\le n-12n0.99≤i≤n−1, the range the proof covers and the use needs.
  3. Lemma A.3, second inequality. It is posed for 1≤J≤n1\le J\le n1≤J≤n, since σ0\sigma_0σ0​ is undefined. The eigenvalues are ordered ∣λ1∣≥⋯≥∣λn∣|\lambda_1|\ge\dots\ge|\lambda_n|∣λ1​∣≥⋯≥∣λn​∣, not ∣λ1∣≤⋯≤∣λn∣|\lambda_1|\le\dots\le|\lambda_n|∣λ1​∣≤⋯≤∣λn​∣ as printed: the printed order is a slip. With it, both inequalities are weaker than Weyl's and cannot support the use on p. 2057, which orders the eigenvalues decreasingly. The decreasing order implies the printed form.
  4. (8.2) and (8.1). (8.2) is stated for every deterministic εn>0\varepsilon_n>0εn​>0 with εn→0\varepsilon_n\to0εn​→0; the proof uses nothing else. (8.1) is stated as μ\muμ-integrability plus local integrability of z↦∫∣log⁡∣w−z∣∣ dμ(w)z\mapsto\int|\log|w-z||\,d\mu(w)z↦∫∣log∣w−z∣∣dμ(w).

A trivializing formalization is ruled out by construction. The goal mentions only AnA_nAn​ and μ\muμ. The proof's comparison ensemble Bn=n diag(λi)B_n=\sqrt n\,\mathrm{diag}(\lambda_i)Bn​=n​diag(λi​) is not a hypothesis. No statement takes a log-potential or a log-determinant as an input that could be fixed to make it hold.

The companion mission (Theorem 1.5, universality) poses Theorem 2.1 (the replacement principle), Lemma 1.7 (tightness) and Lemma 4.1 (least singular value), which the proof here also uses. They are not restated here. Once that mission is published they belong here as references. Contributions are welcome on the linear-algebra layer (Lemma A.3, multiplicative Weyl majorization), the Fubini argument of (8.1), and the singular-value tail bound (8.3). These are reusable well beyond this paper.

Selected references

  • T. Tao, V. Vu (appendix by M. Krishnapur), Random matrices: Universality of ESDs and the circular law, Ann. Probab. 38(5) (2010) 2023–2065. https://doi.org/10.1214/10-AOP534 (source of this mission; published version).
  • V. L. Girko, The circular law, Teor. Veroyatnost. i Primenen. 29 (1984) 669–679. https://doi.org/10.1137/1129095
  • Z. D. Bai, Circular law, Ann. Probab. 25(1) (1997) 494–529. https://doi.org/10.1214/aop/1024404298
  • F. Götze, A. Tikhomirov, The circular law for random matrices, Ann. Probab. 38(4) (2010) 1444–1491. https://doi.org/10.1214/09-AOP522
  • T. Tao, V. Vu, Random matrices: the circular law, Commun. Contemp. Math. 10(2) (2008) 261–307. https://doi.org/10.1142/S0219199708002788
  • H. Weyl, Inequalities between the two kinds of eigenvalues of a linear transformation, Proc. Natl. Acad. Sci. USA 35 (1949) 408–411. https://doi.org/10.1073/pnas.35.7.408
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Linear algebraNumerical AnalysisOptimization·Captain: mikedeng1

The Primal-Dual Active Set Strategy as a Semismooth Newton Method III: Global Convergence under the Norm and Cone ConditionsResearch Paper

Motivation

Obstacle problems, contact problems and control-constrained optimal control problems lead, after discretization, to complementarity systems of the form "find y≤ψy \le \psiy≤ψ and λ≥0\lambda \ge 0λ≥0 with Ay+λ=fAy + \lambda = fAy+λ=f and λi(yi−ψi)=0\lambda_i(y_i - \psi_i) = 0λi​(yi​−ψi​)=0". The primal-dual active set strategy solves such systems by guessing which constraints are active, solving one linear system, and updating the guess. It is widely used because each iteration is a single linear solve and, in practice, it terminates after few iterations.

Hintermüller, Ito and Kunisch (SIAM J. Optim. 13(3), 2002; authors' version hal-01660511v1) showed that the strategy is a semismooth Newton method applied to a reformulation of the complementarity system with a max-function. This gives local superlinear convergence. For global convergence, from arbitrary initial data, the paper gives three sufficient conditions on the matrix AAA: the M-matrix case (Theorem 3.2), the condition of Theorem 3.3 treated in this mission, and small perturbations of M-matrices (Theorem 3.4). Theorem 3.3 is the one that goes beyond sign patterns: it covers P-matrices whose blocks satisfy a norm bound and a cone condition, with the sum of the components of yyy serving as a merit function.

Setting

Fix n≥0n \ge 0n≥0, a matrix A∈Rn×nA \in \mathbb{R}^{n \times n}A∈Rn×n, vectors f,ψ∈Rnf, \psi \in \mathbb{R}^nf,ψ∈Rn and a parameter c>0c > 0c>0. Vectors are ordered componentwise. The problem is

Ay+λ=f,λ−max⁡(0,λ+c(y−ψ))=0,(3.1)Ay + \lambda = f, \qquad \lambda - \max\big(0, \lambda + c(y - \psi)\big) = 0, \tag{3.1}Ay+λ=f,λ−max(0,λ+c(y−ψ))=0,(3.1)

with the maximum taken componentwise. In the paper's notation the matrix is AAA and the index sets are calligraphic: A\mathcal{A}A (active) and I\mathcal{I}I (inactive).

A P-matrix is a square matrix all of whose principal minors are positive. For an index set I⊆{1,…,n}\mathcal{I} \subseteq \{1,\dots,n\}I⊆{1,…,n} with complement A\mathcal{A}A, AIA_{\mathcal{I}}AI​ is the principal submatrix of AAA on I\mathcal{I}I, and AIAA_{\mathcal{I}\mathcal{A}}AIA​ is the submatrix with rows in I\mathcal{I}I and columns in A\mathcal{A}A. For a P-matrix every AIA_{\mathcal{I}}AI​ is invertible.

The primal-dual active set algorithm starts from an arbitrary pair (y0,λ0)(y^0, \lambda^0)(y0,λ0). Given (yk,λk)(y^k, \lambda^k)(yk,λk) it sets

Ak={i:λik+c(yik−ψi)>0},Ik={i:λik+c(yik−ψi)≤0},\mathcal{A}_k = \{ i : \lambda^k_i + c(y^k_i - \psi_i) > 0 \}, \qquad \mathcal{I}_k = \{ i : \lambda^k_i + c(y^k_i - \psi_i) \le 0 \},Ak​={i:λik​+c(yik​−ψi​)>0},Ik​={i:λik​+c(yik​−ψi​)≤0},

and determines (yk+1,λk+1)(y^{k+1}, \lambda^{k+1})(yk+1,λk+1) from

Ayk+1+λk+1=f,yk+1=ψ on Ak,λk+1=0 on Ik.Ay^{k+1} + \lambda^{k+1} = f, \qquad y^{k+1} = \psi \text{ on } \mathcal{A}_k, \qquad \lambda^{k+1} = 0 \text{ on } \mathcal{I}_k .Ayk+1+λk+1=f,yk+1=ψ on Ak​,λk+1=0 on Ik​.

For a rectangular matrix BBB, B+B_+B+​ is the matrix of the positive parts max⁡(0,bij)\max(0, b_{ij})max(0,bij​) of its entries, and ∥⋅∥1\|\cdot\|_1∥⋅∥1​ is the matrix norm subordinate to the one-norms, i.e. the maximal absolute column sum. Thus ∥B+∥1=max⁡j∑imax⁡(0,bij)\|B_+\|_1 = \max_j \sum_i \max(0, b_{ij})∥B+​∥1​=maxj​∑i​max(0,bij​).

Formalization targets

Goal: Theorem 3.3

If AAA is a P-matrix and for every partition of {1,…,n}\{1, \dots, n\}{1,…,n} into disjoint sets I\mathcal{I}I and A\mathcal{A}A

∥(AI−1AIA)+∥1<1and∑i∈I(AI−1v)i≥0  for all v≥0,\big\|(A_{\mathcal{I}}^{-1} A_{\mathcal{I}\mathcal{A}})_+\big\|_1 < 1 \qquad\text{and}\qquad \sum_{i \in \mathcal{I}} (A_{\mathcal{I}}^{-1} v)_i \ge 0 \ \text{ for all } v \ge 0,​(AI−1​AIA​)+​​1​<1andi∈I∑​(AI−1​v)i​≥0  for all v≥0,

then for every solution x∗=(y∗,λ∗)x^* = (y^*, \lambda^*)x∗=(y∗,λ∗) of (3.1) and every run of the algorithm, xk=(yk,λk)→x∗x^k = (y^k, \lambda^k) \to x^*xk=(yk,λk)→x∗.

Milestones

The proof in the paper passes through the following claims, in order: the sign facts λIkk≤0\lambda^k_{\mathcal{I}_k} \le 0λIk​k​≤0 and yAkk≥ψAky^k_{\mathcal{A}_k} \ge \psi_{\mathcal{A}_k}yAk​k​≥ψAk​​ for k≥1k \ge 1k≥1; the update formula on the inactive set derived from the Newton system (3.3); inequality (3.6); inequality (3.7),

∑i=1n(yik+1−yik)≤−∥yk−ψ∥1,Ak+∥(AIk−1AIkAk)+∥1 ∥yk−ψ∥1,Ak;\sum_{i=1}^n (y^{k+1}_i - y^k_i) \le -\|y^k - \psi\|_{1,\mathcal{A}_k} + \big\|(A_{\mathcal{I}_k}^{-1} A_{\mathcal{I}_k\mathcal{A}_k})_+\big\|_1 \, \|y^k - \psi\|_{1,\mathcal{A}_k};i=1∑n​(yik+1​−yik​)≤−∥yk−ψ∥1,Ak​​+​(AIk​−1​AIk​Ak​​)+​​1​∥yk−ψ∥1,Ak​​;

and the fact that a repeated active set yields a solution of (3.1).

Companion statements

Existence of a run from every initial pair when AAA is a P-matrix; Remark 3.3 (an M-matrix satisfies the conditions of Theorem 3.3); and the stronger conclusion of the proof, that every run reaches x∗x^*x∗ after finitely many steps.

Significance

Theorem 3.3 gives global convergence of the primal-dual active set strategy for a class of matrices that is not restricted to M-matrices, and identifies M(y)=∑iyi\mathcal{M}(y) = \sum_i y_iM(y)=∑i​yi​ as a merit function under its two conditions. The paper points out that the result applies to discretizations of control-constrained optimal control problems, where the system matrix is generally not an M-matrix, and Remark 3.3 shows that the M-matrix case falls under it.

The result is proved on paper; none of it has been machine-checked. A formal development would produce a checked global convergence theorem for a semismooth Newton method, together with reusable statements about principal blocks of P-matrices and the subordinate one-norm of a positive part. The step from "the merit function does not increase" to convergence is the part of the paper's argument that a formal proof has to make fully precise (see below).

Difficulty

The obvious route is the paper's: show that ∑iyik\sum_i y^k_i∑i​yik​ decreases strictly until the active set repeats, conclude that no active set can recur, and use finiteness of the set of possible active sets. The inequalities (3.6) and (3.7) give only a non-strict decrease. The paper's assertion "<0< 0<0, unless yk+1=yky^{k+1} = y^kyk+1=yk" does not follow from them as written: the right-hand side of (3.7) vanishes when yk=ψy^k = \psiyk=ψ on Ak\mathcal{A}_kAk​, and the cone condition permits ∑i∈I(AI−1λIk)i=0\sum_{i \in \mathcal{I}} (A_{\mathcal{I}}^{-1}\lambda^k_{\mathcal{I}})_i = 0∑i∈I​(AI−1​λIk​)i​=0 with λIk≠0\lambda^k_{\mathcal{I}} \neq 0λIk​=0. A complete proof has to exclude cycling among active sets at equal merit value by a further argument. That is the central difficulty of this mission, and the reason the strict part of (3.7) is not stated as a milestone.

Formalization scope

Vectors are Fin n → ℝ and matrices Matrix (Fin n) (Fin n) ℝ; index sets are Finset (Fin n) and blocks are submatrices along the subtype inclusion. The P-matrix predicate is the published definition RobinsonSR.Schur.IsPMatrix (every nonempty principal minor is positive). The algorithm is encoded as a relation between consecutive iterates, and a run is an infinite sequence related step by step with arbitrary initial data; the stopping option of the algorithm is not modelled. The solution of (3.1) enters as a hypothesis: for a P-matrix it exists and is unique, so no assumption beyond the paper is made. Convergence is convergence of (yk,λk)(y^k, \lambda^k)(yk,λk) in Rn×Rn\mathbb{R}^n \times \mathbb{R}^nRn×Rn. The parameter c>0c > 0c>0 is the paper's standing assumption.

Conventions committed to:

  • both conditions of Theorem 3.3 range over every index set, including I=∅\mathcal{I} = \emptysetI=∅ and I={1,…,n}\mathcal{I} = \{1,\dots,n\}I={1,…,n};
  • ∥B+∥1\|B_+\|_1∥B+​∥1​ is the maximal column sum of the positive part, computed as a supremum in R≥0\mathbb{R}_{\ge 0}R≥0​, and equals 000 for a matrix with no columns;
  • the cone condition is ∑i∈I(AI−1v)i≥0\sum_{i\in\mathcal{I}}(A_{\mathcal{I}}^{-1}v)_i \ge 0∑i∈I​(AI−1​v)i​≥0 for v≥0v \ge 0v≥0, not entrywise nonnegativity of AI−1A_{\mathcal{I}}^{-1}AI−1​;
  • the milestones on the iterates hold from k=1k = 1k=1 on, as in the paper.

The goal does not assume a decrease of the merit function or finite termination; a formalization that adds either as a hypothesis would be trivial and is ruled out. The run-existence companion shows that the hypothesis "let (yk,λk)(y^k,\lambda^k)(yk,λk) be a run" can be met, so the goal is not vacuous.

A complete development needs: invertibility of principal blocks of a P-matrix, the block form of one step, the elementary estimate of ∑i(Bx)i\sum_i (B x)_i∑i​(Bx)i​ by ∥B+∥1∑jxj\|B_+\|_1 \sum_j x_j∥B+​∥1​∑j​xj​ for x≥0x \ge 0x≥0, and an argument that excludes cycling. Proofs of any milestone, of the companion statements, or of the goal through an argument other than the paper's are welcome.

Selected references

  • M. Hintermüller, K. Ito, K. Kunisch, The primal-dual active set strategy as a semismooth Newton method, SIAM J. Optim. 13(3):865–888, 2002. https://doi.org/10.1137/S1052623401383558 ; authors' version https://hal.science/hal-01660511v1
  • A. Berman, R. J. Plemmons, Nonnegative Matrices in the Mathematical Sciences, Academic Press, 1979 (SIAM Classics reprint 1994). https://doi.org/10.1137/1.9781611971262
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Operations ResearchOptimizationProbability·Captain: mikedeng1

Supply Chain Coordination Under Channel Rebates with Sales Effort Effects III: Returns Alone, Linear Rebates Alone or Target Rebates Alone Cannot Coordinate Effort and QuantityResearch Paper

Motivation

Manufacturers of computer hardware, software and automobiles routinely pay channel rebates to their retailers: a payment for each unit the retailer sells to end customers, either for every unit (a linear rebate) or only for units beyond a target (a target rebate). In the same industries manufacturers also accept returns, crediting the retailer for unsold stock. Both instruments are meant to make the retailer act in the interest of the whole supply chain. When the retailer's sales effort drives demand and cannot be written into a contract, the question is which of these instruments, alone or combined, can make the retailer choose the effort and the stock that maximize total chain profit.

T. A. Taylor, Supply Chain Coordination Under Channel Rebates with Sales Effort Effects, Management Science 48(8) (2002) (DOI 10.1287/mnsc.48.8.992.168), answers this in a newsvendor model with multiplicative effort. Its Theorem 2 shows that a target rebate combined with returns can coordinate. This mission formalizes the complementary negative result, the paper's Proposition 2: no single instrument suffices. It is related to the impossibility shown by Cachon and Lariviere for revenue sharing with retailer effort (Management Science 51(1), 2005), but concerns different instruments.

Setting

A retailer orders a quantity Q≥0Q \ge 0Q≥0 and exerts a sales effort e≥0e \ge 0e≥0 before demand is observed. Demand is eξe\xieξ, where ξ\xiξ is a random variable with a density φ\varphiφ on R\mathbb RR that vanishes on (−∞,0)(-\infty, 0)(−∞,0) and is strictly positive on [0,∞)[0, \infty)[0,∞) (Assumption A4), with finite mean. Write

Φ(Q)=∫0Qφ(ξ) dξ,Γ(Q)=∫0Qξ dΦ(ξ).\Phi(Q) = \int_0^Q \varphi(\xi)\,d\xi, \qquad \Gamma(Q) = \int_0^Q \xi\,d\Phi(\xi).Φ(Q)=∫0Q​φ(ξ)dξ,Γ(Q)=∫0Q​ξdΦ(ξ).

Effort costs V(e)V(e)V(e), where V(0)=0V(0) = 0V(0)=0 and VVV is strictly increasing and strictly convex on [0,∞)[0, \infty)[0,∞) (Assumption A5; the paper declares all such properties strict). The retail price ppp, the manufacturer's unit cost ccc, the wholesale price www and the salvage value sss satisfy 0<c<w<p0 < c < w < p0<c<w<p and s<cs < cs<c (Assumption A1); sss may be negative.

The integrated channel earns

Π(Q,e)=−cQ+pEmin⁡(Q,eξ)+sE(Q−eξ)+−V(e),\Pi(Q, e) = -cQ + pE\min(Q, e\xi) + sE(Q - e\xi)^+ - V(e),Π(Q,e)=−cQ+pEmin(Q,eξ)+sE(Q−eξ)+−V(e),

and (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ) denotes a maximizer of Π\PiΠ over Q≥0Q \ge 0Q≥0, e≥0e \ge 0e≥0. Under a contract with wholesale price www, rebate uuu per unit sold beyond the target TTT, and return credit bbb per unsold unit, the retailer earns

R(Q,e∣T)=−wQ+pEmin⁡(Q,eξ)+uE(min⁡(Q,eξ)−T)++bE(Q−eξ)+−V(e).R(Q, e \mid T) = -wQ + pE\min(Q, e\xi) + uE(\min(Q, e\xi) - T)^+ + bE(Q - e\xi)^+ - V(e).R(Q,e∣T)=−wQ+pEmin(Q,eξ)+uE(min(Q,eξ)−T)++bE(Q−eξ)+−V(e).

Returns alone is u=0u = 0u=0 with b∈[s,w)b \in [s, w)b∈[s,w); a target rebate alone is u>0u > 0u>0, T≥0T \ge 0T≥0 with no returns, b=sb = sb=s (the retailer salvages leftovers herself); a linear rebate alone is the case T=0T = 0T=0 of the latter. Channel coordination means that the retailer's optimal decisions under the contract are the integrated channel's (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ).

Formalization targets

Goal: Proposition 2 (p. 1003)

For every b∈[s,w)b \in [s, w)b∈[s,w), every u>0u > 0u>0 and every T≥0T \ge 0T≥0,

(Qˉ,eˉ)∉arg⁡max⁡Q,e≥0R(⋅,⋅∣T)  under (u,b,T)=(0,b,0), (u,s,0), (u,s,T).(\bar Q, \bar e) \notin \arg\max_{Q, e \ge 0} R(\cdot, \cdot \mid T)\ \text{ under } (u, b, T) = (0, b, 0),\ (u, s, 0),\ (u, s, T).(Qˉ​,eˉ)∈/argQ,e≥0max​R(⋅,⋅∣T)  under (u,b,T)=(0,b,0), (u,s,0), (u,s,T).

The three clauses are returns alone, a linear rebate alone, and a target rebate alone. The statement asserts that (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ) is not among the retailer's maximizers at all, which is stronger than saying that the retailer's optimum is not exactly {(Qˉ,eˉ)}\{(\bar Q, \bar e)\}{(Qˉ​,eˉ)}.

Milestones

  1. §4.1, p. 999. An integrated optimum with eˉ>0\bar e > 0eˉ>0 satisfies Qˉ=eˉ Qˉ0\bar Q = \bar e\,\bar Q_0Qˉ​=eˉQˉ​0​ with Φ(Qˉ0)=(p−c)/(p−s)\Phi(\bar Q_0) = (p - c)/(p - s)Φ(Qˉ​0​)=(p−c)/(p−s), V′(eˉ)=(p−s)Γ(Qˉ0)V'(\bar e) = (p - s)\Gamma(\bar Q_0)V′(eˉ)=(p−s)Γ(Qˉ​0​), and Π(Qˉ,eˉ)=eˉV′(eˉ)−V(eˉ)\Pi(\bar Q, \bar e) = \bar e V'(\bar e) - V(\bar e)Π(Qˉ​,eˉ)=eˉV′(eˉ)−V(eˉ).
  2. Lemma 2, p. 1000. For every effort e≥0e \ge 0e≥0, the retailer's optimal orders under (w,u,b,T)(w, u, b, T)(w,u,b,T) are {eQ‾0}\{e\underline Q_0\}{eQ​0​}, {eQ‾1}\{e\underline Q_1\}{eQ​1​} or {eQ‾0,eQ‾1}\{e\underline Q_0, e\underline Q_1\}{eQ​0​,eQ​1​} as e<,>,=T/τe <, >, = T/\taue<,>,=T/τ, where Φ(Q‾0)=(p−w)/(p−b)\Phi(\underline Q_0) = (p - w)/(p - b)Φ(Q​0​)=(p−w)/(p−b), Φ(Q‾1)=(p+u−w)/(p+u−b)\Phi(\underline Q_1) = (p + u - w)/(p + u - b)Φ(Q​1​)=(p+u−w)/(p+u−b) and τ\tauτ is the indifference target of the quantity-only problem.
  3. §4.3, p. 1001. For T>0T > 0T>0, no optimal pair of the retailer has effort exactly T/τT/\tauT/τ.

Significance

Proposition 2 is the converse half of the paper's main message. Together with Theorem 2 it says that the combination of a target rebate with returns is the minimal coordinating scheme among those considered: each ingredient alone fails, under general demand and general effort cost, not only in a parametric example. The proposition also frames the paper's remark that a linear rebate combined with returns coordinates only at u=w−cu = w - cu=w−c and b=s+w−cb = s + w - cb=s+w−c, which leaves the manufacturer no profit.

The result is proved in the paper; nothing in this mission is open mathematics. No part of it has been machine-checked before. The formal development adds a precise account of what the paper's argument needs: the existence and interiority of the integrated optimum, the differentiability of the effort cost, and a full characterization of the retailer's optimal order for a general positive density, which the paper states but proves only for the quantity-only case.

Difficulty

The obvious route compares first-order conditions, but neither problem is globally smooth. The retailer's profit in QQQ has an upward kink at Q=TQ = TQ=T, so it is concave only piecewise and its maximizer is decided by a comparison of two local maxima, not by a first-order condition. Consequently the retailer's value as a function of effort, A(e∣T)=max⁡QR(Q,e∣T)A(e \mid T) = \max_Q R(Q, e \mid T)A(e∣T)=maxQ​R(Q,e∣T), switches branch at e=T/τe = T/\taue=T/τ and is neither concave nor differentiable there. Showing that (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ) is not a retailer optimum therefore requires the global order characterization (Lemma 2) in place of a local condition, a separate argument for the knife-edge effort T/τT/\tauT/τ, and differentiability of expectations such as Emin⁡(Q,eξ)E\min(Q, e\xi)Emin(Q,eξ) in the effort level under only a finite-mean assumption. The paper's own proof cites Lemma 1 where Lemma 2 with b=sb = sb=s is needed.

Formalization scope

The density form of A4 is literal: a measurable φ\varphiφ, zero on negatives, positive on [0,∞)[0, \infty)[0,∞), integrating to one, with ξφ(ξ)\xi\varphi(\xi)ξφ(ξ) integrable. The law of ξ\xiξ is Lebesgue measure with density φ\varphiφ; the law of eξe\xieξ is its image under x↦exx \mapsto exx↦ex. The expectations Emin⁡(Q,eξ)E\min(Q, e\xi)Emin(Q,eξ) and E(Q−eξ)+E(Q - e\xi)^+E(Q−eξ)+ are the published CachonCoord.Newsvendor.expSales and expLeftover of that image law; the rebate term is the integral of max⁡(min⁡(Q,x)−T,0)\max(\min(Q, x) - T, 0)max(min(Q,x)−T,0). Critical fractiles Φ−1(⋅)\Phi^{-1}(\cdot)Φ−1(⋅) and the threshold τ\tauτ are stated by their defining equations, never as inverse functions. "Optimal" means a maximizer over Q≥0Q \ge 0Q≥0 (and e≥0e \ge 0e≥0).

Two hypotheses are pinned relative to the page and disclosed in each statement. The effort cost carries a derivative V′V'V′ with VVV differentiable at every e>0e > 0e>0: the paper writes (∂/∂e)V(\partial/\partial e)V(∂/∂e)V in every first-order condition without stating it, and without it a kink of VVV at eˉ\bar eeˉ could let both the channel and the retailer stop at eˉ\bar eeˉ. The integrated optimum is a hypothesis, as the paper assumes its existence in words, and it is required to have eˉ>0\bar e > 0eˉ>0, as the paper's optimum is interior. Without eˉ>0\bar e > 0eˉ>0 the channel would sell nothing, and a degenerate statement would follow.

A trivializing reading is ruled out: the goal does not say that the retailer's first-order conditions fail at (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ), nor that her optimum is not unique; it says that (Qˉ,eˉ)(\bar Q, \bar e)(Qˉ​,eˉ) is not a retailer maximizer.

Needed infrastructure: differentiation under the integral of Emin⁡(Q,eξ)E\min(Q, e\xi)Emin(Q,eξ) and E(min⁡(Q,eξ)−T)+E(\min(Q, e\xi) - T)^+E(min(Q,eξ)−T)+ in eee, strict monotonicity of Φ\PhiΦ and Γ\GammaΓ on [0,∞)[0, \infty)[0,∞), and the piecewise-concavity analysis of the newsvendor with a target rebate. These pieces are reusable for the sibling missions of this paper and for other effort-dependent newsvendor models. Proofs of the milestones and of supporting lemmas on these expectations are welcome.

Selected references

  • T. A. Taylor, Supply Chain Coordination Under Channel Rebates with Sales Effort Effects, Management Science 48(8):992–1007, 2002. https://doi.org/10.1287/mnsc.48.8.992.168
  • G. P. Cachon, M. A. Lariviere, Supply Chain Coordination with Revenue-Sharing Contracts: Strengths and Limitations, Management Science 51(1):30–44, 2005. https://doi.org/10.1287/mnsc.1040.0215
  • G. P. Cachon, Supply Chain Coordination with Contracts, in Handbooks in Operations Research and Management Science 11, 2003. https://doi.org/10.1016/S0927-0507(03)11006-7
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Linear algebraProbabilityRandom Matrix Theory·Captain: mikedeng1

Random Matrices: Universality of ESDs and the Circular Law 1: For i.i.d. Zero-Mean Unit-Variance Entries, the ESD of (M_n + X_n)/√n Does Not Depend on the Entry Distribution in the LimitResearch Paper

Motivation

The eigenvalues of a large random matrix with independent entries follow deterministic limiting laws. For non-Hermitian matrices with i.i.d. entries of mean zero and variance one, the circular law asserts that the eigenvalues of 1nXn\frac1{\sqrt n}X_nn​1​Xn​ become uniformly distributed on the unit disk. These laws are used across statistics (covariance and signal-plus-noise models), numerical analysis (spectra of perturbed matrices), mathematical physics and the theory of neural networks; what makes them usable is universality: the limit depends only on the mean and variance of the entries, not on their distribution.

Timeline of the circular law and its universality:

  • 1965: Ginibre computes the joint eigenvalue density for complex Gaussian entries; Mehta (1967) derives the circular law in that case.
  • 1984: Girko proposes a general approach via log-determinants; his argument has gaps.
  • 1997: Bai gives the first rigorous proof for continuous entries with finite (2+η)(2+\eta)(2+η)-th moment and bounded density.
  • 2007–2008: Götze–Tikhomirov, Pan–Zhou and Tao–Vu remove the density assumption under extra moment conditions, using lower bounds on the least singular value.
  • 2010: Tao and Vu (Ann. Probab. 38 (2010) 2023–2065) prove universality under the minimal assumption of finite variance, and with a deterministic shift MnM_nMn​; the circular law in full generality follows.

This mission formalizes the main universality theorem of that paper (Theorem 1.5) together with the lemmas its proof rests on.

Setting

Let AAA be an n×nn\times nn×n complex matrix with eigenvalues λ1,…,λn\lambda_1,\dots,\lambda_nλ1​,…,λn​, counted with algebraic multiplicity. Its empirical spectral distribution (ESD) is the probability measure

μA=1n∑i=1nδλi\mu_A=\frac1n\sum_{i=1}^n\delta_{\lambda_i}μA​=n1​i=1∑n​δλi​​

on C\mathbb CC. The Hilbert–Schmidt norm is ∥A∥22=trace(AA∗)=∑i,j∣aij∣2\|A\|_2^2=\mathrm{trace}(AA^*)=\sum_{i,j}|a_{ij}|^2∥A∥22​=trace(AA∗)=∑i,j​∣aij​∣2.

A complex random variable xxx has zero mean and unit variance if Ex=0\mathbf E x=0Ex=0 and E∣x∣2=1\mathbf E|x|^2=1E∣x∣2=1. Given such xxx and yyy, let Xn=(xij)1≤i,j≤nX_n=(x_{ij})_{1\le i,j\le n}Xn​=(xij​)1≤i,j≤n​ and Yn=(yij)1≤i,j≤nY_n=(y_{ij})_{1\le i,j\le n}Yn​=(yij​)1≤i,j≤n​ have entries that are i.i.d. copies of xxx, resp. yyy. Let MnM_nMn​ be deterministic n×nn\times nn×n matrices with

sup⁡n1n2∥Mn∥22<∞,(1.3)\sup_n\frac1{n^2}\|M_n\|_2^2<\infty,\qquad(1.3)nsup​n21​∥Mn​∥22​<∞,(1.3)

and set An=Mn+XnA_n=M_n+X_nAn​=Mn​+Xn​, Bn=Mn+YnB_n=M_n+Y_nBn​=Mn​+Yn​.

Measures on C\mathbb CC carry the vague topology: test functions are continuous compactly supported f:C→Rf:\mathbb C\to\mathbb Rf:C→R. The random measures μ1nAn−μ1nBn\mu_{\frac1{\sqrt n}A_n}-\mu_{\frac1{\sqrt n}B_n}μn​1​An​​−μn​1​Bn​​ converge to zero in probability if, for every test function fff and ε>0\varepsilon>0ε>0,

P(∣∫f dμ1nAn−∫f dμ1nBn∣≥ε)→0,\mathbf P\Bigl(\Bigl|\int f\,d\mu_{\frac1{\sqrt n}A_n}-\int f\,d\mu_{\frac1{\sqrt n}B_n}\Bigr|\ge\varepsilon\Bigr)\to0,P(​∫fdμn​1​An​​−∫fdμn​1​Bn​​​≥ε)→0,

and almost surely if, with probability one, the difference of integrals tends to zero for every test function. The additional hypothesis

(1.4)μ(1nMn−zI)(1nMn−zI)∗ converges for almost every z∈C(1.4)\qquad \mu_{(\frac1{\sqrt n}M_n-zI)(\frac1{\sqrt n}M_n-zI)^*}\ \text{converges for almost every } z\in\mathbb C(1.4)μ(n​1​Mn​−zI)(n​1​Mn​−zI)∗​ converges for almost every z∈C

concerns only the deterministic shift.

Formalization targets

Goal: Theorem 1.5 (universality principle)

μ1nAn−μ1nBn⟶0 in probability;(1.4) ⟹ μ1nAn−μ1nBn⟶0 almost surely.\mu_{\frac1{\sqrt n}A_n}-\mu_{\frac1{\sqrt n}B_n}\longrightarrow0\ \text{in probability};\qquad (1.4)\ \Longrightarrow\ \mu_{\frac1{\sqrt n}A_n}-\mu_{\frac1{\sqrt n}B_n}\longrightarrow0\ \text{almost surely}.μn​1​An​​−μn​1​Bn​​⟶0 in probability;(1.4) ⟹ μn​1​An​​−μn​1​Bn​​⟶0 almost surely.

The statement involves no limiting law: it compares two ensembles, so it holds whether or not μ1nAn\mu_{\frac1{\sqrt n}A_n}μn​1​An​​ itself converges.

Milestones

The proof combines three results, each a milestone:

  • Theorem 2.1 (replacement principle), in probability and almost surely: if 1n2(∥An∥22+∥Bn∥22)\frac1{n^2}(\|A_n\|_2^2+\|B_n\|_2^2)n21​(∥An​∥22​+∥Bn​∥22​) is bounded and, for almost every zzz, 1nlog⁡∣det⁡(1nAn−zI)∣−1nlog⁡∣det⁡(1nBn−zI)∣→0\frac1n\log|\det(\frac1{\sqrt n}A_n-zI)|-\frac1n\log|\det(\frac1{\sqrt n}B_n-zI)|\to0n1​log∣det(n​1​An​−zI)∣−n1​log∣det(n​1​Bn​−zI)∣→0, then μ1nAn−μ1nBn→0\mu_{\frac1{\sqrt n}A_n}-\mu_{\frac1{\sqrt n}B_n}\to0μn​1​An​​−μn​1​Bn​​→0.
  • Lemma 1.7 (tightness): 1n2∥An∥22\frac1{n^2}\|A_n\|_2^2n21​∥An​∥22​ and ∫∣z∣2 dμ1nAn\int|z|^2\,d\mu_{\frac1{\sqrt n}A_n}∫∣z∣2dμn​1​An​​ are almost surely bounded.
  • Proposition 2.2 (converging determinant): for every zzz, the log-determinant difference (2.2) tends to zero in probability, and almost surely if (1.4) holds at zzz.

Supporting milestones: the linear-algebra Lemmas A.1 (interlacing), A.2 (Weyl's second-moment inequality) and A.4 (negative second moment); the probabilistic dominated convergence Lemma 3.1; Girko's identity (Lemma 3.3); the least singular value bound (Lemma 4.1); the lower tail bound for distances to subspaces (Proposition 5.1); and the high- and low-dimensional row contributions (Lemmas 4.2 and 4.3).

Significance

The result. Theorem 1.5 with Mn=0M_n=0Mn​=0 and yyy complex Gaussian transfers Mehta's computation for the Ginibre ensemble to every zero-mean unit-variance entry distribution: this is the circular law under the optimal moment assumption (Theorem 1.10 of the paper). With nonzero MnM_nMn​ it gives universality for perturbed and non-centred matrices, including results with entries of nonzero mean. The replacement principle (Theorem 2.1) is a general tool: it reduces vague convergence of non-Hermitian ESDs to convergence of log-determinants and applies to ensembles without independent entries.

Formalizing it. The theorem is proved in the paper; no part of it, and no ESD of a non-Hermitian matrix, has been formalized. The mission produces a machine-checked statement of universality in the vague topology and a library of reusable pieces: ESDs of complex matrices, convergence of random measures in probability and almost surely, interlacing and Weyl-type inequalities for singular values, and Girko's identity. Lemmas 4.1 and 5.1 rest on results the paper cites (Tao–Vu's least singular value bound, Talagrand's concentration inequality); formalizing those is part of the work.

Difficulty

The ESD of a non-Hermitian matrix is not stable under small perturbations: eigenvalues of a nearly singular matrix can move a long way, so the moment method and the Stieltjes transform method that settle the Hermitian case do not apply. The log-determinant 1nlog⁡∣det⁡(1nAn−zI)∣\frac1n\log|\det(\frac1{\sqrt n}A_n-zI)|n1​log∣det(n​1​An​−zI)∣ is a stable proxy only if the small singular values of 1nAn−zI\frac1{\sqrt n}A_n-zIn​1​An​−zI are controlled, and with only a second moment on the entries the entries can be unbounded, which defeats standard concentration arguments. The obvious approach, comparing AnA_nAn​ and BnB_nBn​ by swapping entries one at a time, needs smooth bounded statistics of the matrix, and the log-determinant is neither: it is unbounded near singular matrices.

Formalization scope

Matrices are Matrix (Fin n) (Fin n) ℂ; eigenvalues are the roots of the characteristic polynomial as a multiset, so the ESD counts algebraic multiplicity; for n=0n=0n=0 the ESD is the zero measure, harmless because every statement is asymptotic. The Hilbert–Schmidt norm is written as an explicit sum, not Mathlib's default (sup) matrix norm. Singular values are Mathlib's zero-indexed LinearMap.singularValues, shifted so that σ1\sigma_1σ1​ is the largest.

Conventions committed to:

  • Coupling. XnX_nXn​ is the top-left n×nn\times nn×n corner of one infinite i.i.d. array; the almost-sure statements depend on this choice, the in-probability ones do not. No relation between the xxx- and yyy-arrays is assumed.
  • (1.4) asks for a probability measure as the vague limit; under (1.3) any vague limit of these ESDs is one.
  • Almost sure convergence of ESDs uses one null set for all test functions.
  • Junk values. Lean has log⁡0=0\log 0=0log0=0 and 1/0=01/0=01/0=0. Theorem 2.1 therefore states its "determinants are nonzero" clause explicitly, and Proposition 2.2 concludes it (the paper's (2.2) is −∞-\infty−∞ at a vanishing determinant, so its convergence includes nonvanishing); Lemmas 4.2 and 4.3 assert positivity of the row distances they take logarithms of; Lemma 3.1's (1+δ)(1+\delta)(1+δ)-moment is a lower integral with values in [0,∞][0,\infty][0,∞], since a Bochner integral would vanish for non-integrable functions and make the hypothesis vacuous.
  • O(⋅)O(\cdot)O(⋅) constants are existential and fixed before the quantities they must not depend on: before ε\varepsilonε in Lemmas 4.2 and 4.3, before the sample point in Lemma 4.1, after ccc and before n,d,Wn,d,Wn,d,W and the row shift in Proposition 5.1.
  • Section 4 setting. Lemmas 4.2 and 4.3 carry the standing reductions of §4 as hypotheses: z=0z=0z=0, the row bound (4.2) ∥Zi∥=O(n)\|Z_i\|=O(\sqrt n)∥Zi​∥=O(n​) for n/2≤i≤nn/2\le i\le nn/2≤i≤n (which the paper obtains by permuting rows), and, for Lemma 4.3, convergence of μ1nMnMn∗\mu_{\frac1nM_nM_n^*}μn1​Mn​Mn∗​​. Proposition 5.1 quantifies over every deterministic row shift.
  • Printed slip. On p. 2045 the YiY_iYi​ are called "the rows of 1nBn\frac1{\sqrt n}B_nn​1​Bn​" and then normalized again; here YiY_iYi​ are the rows of BnB_nBn​, matching XiX_iXi​.

A trivializing formalization is excluded: the goal mentions only ESDs, i.i.d. entries, (1.3) and (1.4) — no log-determinant or singular value hypothesis — and its in-probability half holds without (1.4). Lemmas 6.3 and 6.4 and the internal Lemmas 3.2, 3.4, 5.3, 6.1, 6.6, 6.7 and Theorems 5.2, B.1 are not posed. Contributions are welcome on any milestone, and particularly on the deterministic Lemmas A.1, A.2, A.4 and 3.3, which are reusable well beyond this mission.

Selected references

  • T. Tao, V. Vu (appendix by M. Krishnapur), Random matrices: universality of ESDs and the circular law, The Annals of Probability 38(5) (2010) 2023–2065. https://doi.org/10.1214/10-AOP534 (the source; the published article is the citation basis)
  • J. Ginibre, Statistical ensembles of complex, quaternion, and real matrices, J. Math. Phys. 6 (1965) 440–449. https://doi.org/10.1063/1.1704292
  • V. L. Girko, The circular law, Teor. Veroyatnost. i Primenen. 29 (1984) 669–679; English translation Theory Probab. Appl. 29 (1985) 694–706. https://doi.org/10.1137/1129095
  • Z. D. Bai, Circular law, Ann. Probab. 25 (1997) 494–529. https://doi.org/10.1214/aop/1024404298
  • T. Tao, V. Vu, Random matrices: the circular law, Commun. Contemp. Math. 10 (2008) 261–307. https://doi.org/10.1142/S0219199708002788
  • R. B. Dozier, J. W. Silverstein, On the empirical distribution of eigenvalues of large dimensional information-plus-noise-type matrices, J. Multivariate Anal. 98 (2007) 678–694. https://doi.org/10.1016/j.jmva.2006.09.006
17 thms1 active userReviewed
Convex OptimizationOperations ResearchOptimization+1·Captain: mikedeng1

Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion 4: With Random Right-Hand Side and Known Dual Extreme Points, a Distribution in 𝒫 Attains Ẑ(x) = Ẑ_DD(x)Research Paper

Motivation

Two-stage stochastic linear programs model decisions taken before uncertainty is revealed (a first-stage plan xxx) followed by a corrective recourse action once it is. The classical model needs the full probability distribution of the uncertain data. In practice usually only estimates of a few moments are available, and the computed plan can be sensitive to the distribution that is assumed. The minimax (distributionally robust) approach replaces the single distribution by a class of distributions sharing given moments and optimizes against the worst member of the class. Bertsimas, Doan, Natarajan and Teo (Math. Oper. Res. 35(3), 2010) combine this with risk aversion, through a piecewise-linear disutility of the second-stage cost, and show which instances become semidefinite programs.

Besides the optimal value, the worst-case distribution itself is of practical interest: the paper proposes it as a natural distribution for stress-testing a first-stage solution (§4). This mission concerns the case where the right-hand side of the second stage is random, and formalizes the paper's construction of a worst-case distribution that is a genuine member of the moment class.

Setting

A first-stage decision x∈Rnx\in\mathbb R^nx∈Rn is feasible if x∈X={x:Ax=b, x≥0}x\in X=\{x : Ax=b,\ x\ge 0\}x∈X={x:Ax=b, x≥0}. Given a recourse matrix W∈Rr×dW\in\mathbb R^{r\times d}W∈Rr×d, a technology matrix T∈Rr×nT\in\mathbb R^{r\times n}T∈Rr×n, a constant cost vector q∈Rdq\in\mathbb R^dq∈Rd and a realized right-hand side h∈Rrh\in\mathbb R^rh∈Rr, the second-stage cost is the optimal value of the dual of the recourse linear program,

Q(h,x)=max⁡p (h−Tx)′ps.t.W′p≤q.\mathcal Q(h,x)=\max_{p}\ (h-Tx)'p\quad\text{s.t.}\quad W'p\le q .Q(h,x)=pmax​ (h−Tx)′ps.t.W′p≤q.

Risk aversion enters through the disutility U(t)=max⁡k=1,…,K(αkt+βk)\mathbb U(t)=\max_{k=1,\dots,K}(\alpha_k t+\beta_k)U(t)=maxk=1,…,K​(αk​t+βk​) with αk≥0\alpha_k\ge 0αk​≥0. The right-hand side h~\tilde hh~ is random; its distribution is only known to lie in the moment class

P={P:EP[h~]=μ, EP[h~h~′]=Q},\mathcal P=\{P : \mathbb E_P[\tilde h]=\mu,\ \mathbb E_P[\tilde h\tilde h']=Q\},P={P:EP​[h~]=μ, EP​[h~h~′]=Q},

and the worst-case expected disutility is Z^(x)=sup⁡P∈PEP[U(Q(h~,x))]\hat Z(x)=\sup_{P\in\mathcal P}\mathbb E_P[\mathbb U(\mathcal Q(\tilde h,x))]Z^(x)=supP∈P​EP​[U(Q(h~,x))].

The standing assumptions are: complete recourse, {Ww:w≥0}=Rr\{Ww : w\ge 0\}=\mathbb R^r{Ww:w≥0}=Rr; a nonempty dual polyhedron {p:W′p≤q}\{p : W'p\le q\}{p:W′p≤q}; Q≻μμ′Q\succ\mu\mu'Q≻μμ′; and (Assumption 5) the extreme points p1,…,pNp_1,\dots,p_Np1​,…,pN​ of the dual polyhedron are known. Under these, the paper introduces the semidefinite program (16): over symmetric blocks indexed by pairs (k,i)(k,i)(k,i),

Z^DD(x)=max⁡ ∑k=1K∑i=1N(αkpi′vki+vk0i(βk−αkpi′Tx))  s.t.  ∑k,i(Vkivki(vki)′vk0i)=(Qμμ′1),  (Vkivki(vki)′vk0i)⪰0.\hat Z_{DD}(x)=\max\ \sum_{k=1}^K\sum_{i=1}^N\big(\alpha_k p_i'v_k^i+v_{k0}^i(\beta_k-\alpha_k p_i'Tx)\big) \ \ \text{s.t.}\ \ \sum_{k,i}\begin{pmatrix}V_k^i & v_k^i\\ (v_k^i)' & v_{k0}^i\end{pmatrix}=\begin{pmatrix}Q&\mu\\ \mu'&1\end{pmatrix},\ \ \begin{pmatrix}V_k^i & v_k^i\\ (v_k^i)' & v_{k0}^i\end{pmatrix}\succeq 0 .Z^DD​(x)=max k=1∑K​i=1∑N​(αk​pi′​vki​+vk0i​(βk​−αk​pi′​Tx))  s.t.  k,i∑​(Vki​(vki​)′​vki​vk0i​​)=(Qμ′​μ1​),  (Vki​(vki​)′​vki​vk0i​​)⪰0.

From a point of (16) the paper builds the mixed distribution Pm(x)P_m(x)Pm​(x), which with probability vk0iv_{k0}^ivk0i​ draws a normal vector with mean vki/vk0iv_k^i/v_{k0}^ivki​/vk0i​ and covariance (Vkivk0i−vki(vki)′)/(vk0i)2(V_k^iv_{k0}^i-v_k^i(v_k^i)')/(v_{k0}^i)^2(Vki​vk0i​−vki​(vki​)′)/(vk0i​)2.

Formalization targets

Goal: Theorem 3.3 (p. 590)

For every x∈Xx\in Xx∈X there is P∈PP\in\mathcal PP∈P with

EP′[U(Q(h~,x))]≤EP[U(Q(h~,x))]=Z^(x)=Z^DD(x)∀P′∈P,\mathbb E_{P'}\big[\mathbb U(\mathcal Q(\tilde h,x))\big]\le \mathbb E_{P}\big[\mathbb U(\mathcal Q(\tilde h,x))\big]=\hat Z(x)=\hat Z_{DD}(x)\qquad\forall P'\in\mathcal P,EP′​[U(Q(h~,x))]≤EP​[U(Q(h~,x))]=Z^(x)=Z^DD​(x)∀P′∈P,

where the common value is the attained maximum of (16), and the expected disutility is integrable under every member of P\mathcal PP.

Milestones (proof of Theorem 3.3, pp. 590–591)

  1. U(Q(h,x))=max⁡k,i(αk(h−Tx)′pi+βk)\mathbb U(\mathcal Q(h,x))=\max_{k,i}\big(\alpha_k(h-Tx)'p_i+\beta_k\big)U(Q(h,x))=maxk,i​(αk​(h−Tx)′pi​+βk​) for every hhh.
  2. Every P∈PP\in\mathcal PP∈P yields a feasible point of (16) of equal value; hence Z^(x)≤Z^DD(x)\hat Z(x)\le\hat Z_{DD}(x)Z^(x)≤Z^DD​(x).
  3. (16) has an optimal solution whose blocks have vk0i>0v_{k0}^i>0vk0i​>0 or vanish.
  4. For such a feasible point, Pm(x)∈PP_m(x)\in\mathcal PPm​(x)∈P.
  5. EPm(x)[U(Q(h~,x))]\mathbb E_{P_m(x)}[\mathbb U(\mathcal Q(\tilde h,x))]EPm​(x)​[U(Q(h~,x))] is at least the objective value of (16) at that point.

Significance

The result. Theorem 3.3 shows that, once the dual extreme points are known, the worst case over the moment class is attained, and by an explicit finite mixture of normal distributions read off an optimal solution of a semidefinite program. This contrasts with the random-objective case of the same paper (Theorem 2.2), where the bound is in general only approached by a sequence of distributions. The extremal distribution gives a concrete stress-test scenario for a candidate first-stage plan, and the identity Z^(x)=Z^DD(x)\hat Z(x)=\hat Z_{DD}(x)Z^(x)=Z^DD​(x) certifies the value of the inner worst-case problem without appeal to strong duality for the moment problem.

Formalizing it. The result is proved in the paper; to our knowledge no machine-checked version exists. A formal proof needs a measure-theoretic treatment of conditional moments on the cells of a piecewise-linear maximum, the existence of optima of a bounded semidefinite program, and moment computations for multivariate normal mixtures. These pieces are reusable well beyond this paper, for instance in other moment-based distributionally robust models.

Difficulty

The obvious route goes through moment-problem duality: dualize sup⁡P∈P\sup_{P\in\mathcal P}supP∈P​, reformulate the semi-infinite constraint as linear matrix inequalities, and dualize again. That route only gives the inequality chain up to a duality gap and says nothing about attainment over P\mathcal PP. The proof here avoids strong duality, but it must show two separate things: every distribution in P\mathcal PP maps to a feasible point of (16) without loss of value (this needs a measurable choice of the maximizing pair and the conditional moments on each cell), and the optimal point of (16) maps back to a distribution in P\mathcal PP whose value is at least the optimum. The second step fails on blocks with zero weight vk0i=0v_{k0}^i=0vk0i​=0, where no normal component can be formed; those blocks have to be removed from an optimal solution first.

Formalization scope

The Lean development works on Rr\mathbb R^rRr as Fin r → ℝ, with measures in place of the paper's densities. The moment class reuses the published definitions MomentDRO.Conf.HasSecondMoments, meanVec and secondMomentAbout P 0 (uncentred second moment). Q(h,x)\mathcal Q(h,x)Q(h,x), Z^(x)\hat Z(x)Z^(x) and Z^DD(x)\hat Z_{DD}(x)Z^DD​(x) are real sSups; every theorem that uses them states the relevant maximum or bound explicitly, so no statement depends on the junk value of an empty or unbounded supremum. Bordered matrices are Matrix.fromBlocks over Fin r ⊕ Fin 1 and ⪰0\succeq 0⪰0 is Matrix.PosSemidef. The normal components are Mathlib's multivariateGaussian, transported from EuclideanSpace ℝ (Fin r); the mixture weights are ENNReal.ofReal vk0iv_{k0}^ivk0i​.

Readings of the page:

  • As printed, Assumption 2 (complete recourse) and Assumption 3 "for all qqq" are mutually inconsistent when r≥1r\ge 1r≥1. The formalization uses complete recourse and nonemptiness of {p:W′p≤q}\{p : W'p\le q\}{p:W′p≤q} at the one constant qqq of §3.
  • Assumption 1 is not used and is dropped; x∈Xx\in Xx∈X is kept as in the statement.
  • "Without loss of generality vk0i>0v_{k0}^i>0vk0i​>0" is read as: some optimal solution has every block either with vk0i>0v_{k0}^i>0vk0i​>0 or zero.
  • The printed "Vkivk0V_k^iv_{k0}Vki​vk0​", "vk02v_{k0}^2vk02​" and "with probability vk0v_{k0}vk0​" are read with the superscript iii.

The goal asserts that a maximizer over P\mathcal PP exists, not only an equation between suprema: an equation such as EP[⋅]=Z^(x)\mathbb E_P[\cdot]=\hat Z(x)EP​[⋅]=Z^(x) alone could hold through a junk value and is ruled out as the target. No strong-duality hypothesis is assumed, since the proof does not need one. Contributions welcome: the measurable argmax selection, compactness of the feasible set of (16), and second-moment computations for Gaussian mixtures.

Selected references

  • D. Bertsimas, X. V. Doan, K. Natarajan, C.-P. Teo, Models for Minimax Stochastic Linear Optimization Problems with Risk Aversion, Mathematics of Operations Research 35(3):580–602, 2010. https://doi.org/10.1287/moor.1100.0445
  • E. Delage, Y. Ye, Distributionally Robust Optimization Under Moment Uncertainty with Application to Data-Driven Problems, Operations Research 58(3):595–612, 2010. https://doi.org/10.1287/opre.1090.0741
  • J. Dupačová, The minimax approach to stochastic programming and an illustrative application, Stochastics 20(1):73–88, 1987. https://doi.org/10.1080/17442508708833436
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AnalysisControl TheoryOperations Research·Captain: mikedeng1

On Minimizing the Ruin Probability by Investment and Reinsurance II: Equation (5) for g = f′ Has a Unique Strictly Decreasing Solution on [0, ∞)Research Paper

Motivation

An insurance company whose surplus falls below zero is ruined. The probability of ruin is the classical measure of solvency risk in actuarial mathematics. A company can lower it in two ways: it can invest part of its surplus in a risky asset, and it can pass part of each claim to a reinsurer in exchange for part of the premium. Which mix of investment and reinsurance minimises the probability of ruin is a stochastic control problem. It is studied in risk theory and in applied probability, and it is a model case for control problems with jumps.

Hipp and Plum (2000) solved the problem with investment alone. They wrote down the Hamilton–Jacobi–Bellman (HJB) equation and showed that it has a smooth solution. H. Schmidli (2002) added proportional reinsurance. He proved a verification theorem (his Theorem 1) and an existence theorem (his Theorem 2) for the combined problem. This mission formalizes the existence theorem and the analytic statements it rests on.

Setting

Claims arrive as a Poisson process with rate λ>0\lambda>0λ>0. The claim sizes YYY are independent with distribution function GGG, where G(0)=0G(0)=0G(0)=0 and GGG is continuous. The insurer collects premiums at rate c>0c>0c>0. A risky asset follows a geometric Brownian motion with drift μ>0\mu>0μ>0 and volatility σ>0\sigma>0σ>0. Under proportional reinsurance with retention level b∈[0,1]b\in[0,1]b∈[0,1], the insurer pays the fraction bYbYbY of each claim and pays the reinsurer a premium at rate c(b)c(b)c(b). The function c(b)c(b)c(b) is decreasing and continuous, c(1)=0c(1)=0c(1)=0, lim inf⁡b↑1c(b)/(1−b)>0\liminf_{b\uparrow1}c(b)/(1-b)>0liminfb↑1​c(b)/(1−b)>0, and there is b‾>0\underline b>0b​>0 with c(b)>cc(b)>cc(b)>c for b<b‾b<\underline bb<b​ and c(b)≤cc(b)\le cc(b)≤c for b≥b‾b\ge\underline bb≥b​. In particular, full reinsurance costs more than the premium income.

Write fff for an unnormalised survival probability, with f(u)=0f(u)=0f(u)=0 for u<0u<0u<0. The HJB equation (1) of the problem is

sup⁡b∈[0,1]sup⁡A≥0[12σ2A2f′′(u)+(c−c(b)+μA)f′(u)+λ(E[f(u−bY)]−f(u))]=0,\sup_{b\in[0,1]}\sup_{A\ge0}\Big[\tfrac12\sigma^2A^2f''(u)+\big(c-c(b)+\mu A\big)f'(u)+\lambda\big(\mathbb E[f(u-bY)]-f(u)\big)\Big]=0 ,b∈[0,1]sup​A≥0sup​[21​σ2A2f′′(u)+(c−c(b)+μA)f′(u)+λ(E[f(u−bY)]−f(u))]=0,

where AAA is the amount invested. Eliminating AAA and substituting g=f′g=f'g=f′ turns (1) into the integral equation (5):

g(u)=1μ22σ2∫0udxDg(x)+cλ,g(u)=\frac{1}{\dfrac{\mu^2}{2\sigma^2}\displaystyle\int_0^u\frac{dx}{D_g(x)}+\dfrac{c}{\lambda}},g(u)=2σ2μ2​∫0u​Dg​(x)dx​+λc​1​, Dg(x)=inf⁡b∈[0,1][λ(1−G(x/b)+∫0x(1−G((x−z)/b))g(z) dz)−(c−c(b))g(x)].D_g(x)=\inf_{b\in[0,1]}\Big[\lambda\Big(1-G(x/b)+\int_0^x\big(1-G((x-z)/b)\big)g(z)\,dz\Big)-\big(c-c(b)\big)g(x)\Big].Dg​(x)=b∈[0,1]inf​[λ(1−G(x/b)+∫0x​(1−G((x−z)/b))g(z)dz)−(c−c(b))g(x)].

Condition (6) at uuu is Dg(u)>0D_g(u)>0Dg​(u)>0. In Lean, ReinsPremium, ClaimLaw, tail, den (=Dg=D_g=Dg​), SolvesEq5On, H and SolvesHJB (equation (1)) live in the shared module SchmidliRuin.Verif.Setting; HasBoundedDensity, Cond6, SolvesEq3At and SolvesHJBAtZero live in SchmidliRuin.Exist.Setting.

Formalization targets

Goal: Theorem 2 (p. 900)

If GGG has a bounded density, then

∃! g:[0,∞)→R strictly decreasing, solving (5) on [0,∞).\exists!\ g:[0,\infty)\to\mathbb R\ \text{strictly decreasing, solving (5) on }[0,\infty).∃! g:[0,∞)→R strictly decreasing, solving (5) on [0,∞).

Lean states this as two conjuncts: existence, and equality on [0,∞)[0,\infty)[0,∞) of any two strictly decreasing solutions.

Milestones, in attack order

  1. Lemma 3 (p. 894). For a solution fff of (3) on [0,η)[0,\eta)[0,η), the optimal retention is b∗(u)=1b^*(u)=1b∗(u)=1 for small uuu: it is optimal not to reinsure small capital.
  2. Lemma 4 (p. 898). Under a bounded density, (5) has a solution on some [0,ε)[0,\varepsilon)[0,ε), with g(u)=λ/c−αu+o(u)g(u)=\lambda/c-\alpha\sqrt u+o(\sqrt u)g(u)=λ/c−αu​+o(u​) and α=λμ/(σc3/2)\alpha=\lambda\mu/(\sigma c^{3/2})α=λμ/(σc3/2).
  3. Condition (6) near 000 (pp. 898–899): such a local solution satisfies Dg(u)>0D_g(u)>0Dg​(u)>0 for small u>0u>0u>0.
  4. Lemma 5 (p. 899). A decreasing solution on [0,u0)[0,u_0)[0,u0​) with (6) on (0,u0)(0,u_0)(0,u0​) extends to [0,u0][0,u_0][0,u0​], and (6) holds at u0u_0u0​.
  5. Continuation (proof of Theorem 2, p. 901). A solution with (6) on (0,u0](0,u_0](0,u0​] continues to [0,u0+η)[0,u_0+\eta)[0,u0​+η) with (6).
  6. Theorem 1, second sentence (p. 896). For a decreasing solution ggg of (5), f(x)=1+∫0xgf(x)=1+\int_0^xgf(x)=1+∫0x​g is a strictly increasing solution of (1), twice continuously differentiable on (0,∞)(0,\infty)(0,∞).
  7. Theorem 1, last sentence (p. 896). There is at most one strictly increasing, twice continuously differentiable solution of (1) with f(0)=1f(0)=1f(0)=1.

Significance

The existence theorem completes the dynamic-programming solution of the problem. By the verification theorem, f=1+∫0⋅gf=1+\int_0^\cdot gf=1+∫0⋅​g divided by f(∞)f(\infty)f(∞) is the maximal survival probability δ(u)\delta(u)δ(u). The maximisers of the HJB equation define the optimal feedback strategy: the investment A∗(u)=−μf′(u)/(σ2f′′(u))A^*(u)=-\mu f'(u)/(\sigma^2f''(u))A∗(u)=−μf′(u)/(σ2f′′(u)) and the retention level b∗(u)b^*(u)b∗(u). Without Theorem 2 the verification theorem would be a statement about a function that might not exist. Equation (5) is also the basis of the paper's numerical scheme (§5), which iterates (5) to compute δ\deltaδ and the optimal strategies.

Theorem 2 has a published proof. As far as is known, none of the results above has a machine-checked proof, and nothing on Prove2Me states them. The work is to formalize the published argument. This takes the local existence result of Hipp and Plum, which the paper cites rather than reproves, and a continuation argument for a nonlinear Volterra-type equation whose kernel is an infimum over a control parameter. Milestones 6 and 7 are the purely analytic content of the verification theorem that the uniqueness part of Theorem 2 uses. The paper derives milestone 7 stochastically, and an analytic proof is equally welcome.

Difficulty

The denominator Dg(x)D_g(x)Dg​(x) vanishes at x=0x=0x=0: Dg(0)=λ−c g(0)=0D_g(0)=\lambda-c\,g(0)=0Dg​(0)=λ−cg(0)=0, because g(0)=λ/cg(0)=\lambda/cg(0)=λ/c. The integral in (5) is therefore improper at the origin. A Picard iteration started from a constant does not work, because it does not control ∫0udx/Dg(x)\int_0^u dx/D_g(x)∫0u​dx/Dg​(x). One needs the exact square-root rate Dg(x)≍xD_g(x)\asymp\sqrt xDg​(x)≍x​, and Lemma 4 obtains it only through Lemma 3 (no reinsurance near 000) and the Hipp–Plum analysis of the reinsurance-free equation. Lemma 4's proof in the paper is a two-line citation, so a solver must formalize that local existence result.

The second difficulty is continuation. A solution can be continued as long as condition (6) holds, and Lemma 5 has to exclude that (6) degenerates at a finite endpoint. There the infimum over bbb interacts with the vanishing of ggg. The paper's argument uses a Gronwall estimate and the shape of c(b)c(b)c(b) around b‾\underline bb​, and the minimiser bbb in DgD_gDg​ need not be unique or continuous.

Formalization scope

  • Model data. c,λ,μ,σc,\lambda,\mu,\sigmac,λ,μ,σ are real parameters, all strictly positive. The claim law is a probability measure ν\nuν on R\mathbb RR, with G=G=G= cdf ν, ν((−∞,0])=0\nu((-\infty,0])=0ν((−∞,0])=0 and GGG continuous. "Bounded density" means ν\nuν has a measurable density with values in [0,C][0,C][0,C]. E[h(u−bY)]\mathbb E[h(u-bY)]E[h(u−bY)] is ∫h(u−by) dν(y)\int h(u-by)\,d\nu(y)∫h(u−by)dν(y).

  • Premium. c(b)c(b)c(b) is a real function, used on [0,1][0,1][0,1]. The condition lim inf⁡b↑1c(b)/(1−b)>0\liminf_{b\uparrow1}c(b)/(1-b)>0liminfb↑1​c(b)/(1−b)>0 is the lower bound c(b)≥κ(1−b)c(b)\ge\kappa(1-b)c(b)≥κ(1−b) near 111, which is equivalent. A real-valued ccc excludes the case c(0)=∞c(0)=\inftyc(0)=∞ that the paper allows when E[Y]<∞\mathbb E[Y]<\inftyE[Y]<∞.

  • Conventions.

    • 1−G(x/b)1-G(x/b)1−G(x/b) at b=0b=0b=0 is P[0⋅Y>x]=0\mathbb P[0\cdot Y>x]=0P[0⋅Y>x]=0; it is computed by tail, never by Lean's x/0=0x/0=0x/0=0.
    • "ggg solves (5) at uuu" includes that the integral exists: ggg is integrable on [0,u][0,u][0,u], Dg≠0D_g\ne0Dg​=0 on (0,u)(0,u)(0,u), and 1/Dg1/D_g1/Dg​ is integrable on [0,u][0,u][0,u].
    • Every supremum of the paper is a least upper bound (IsLUB … 0). Lean's sSup would return 000 on an unbounded family, for example when f′′≥0f''\ge0f′′≥0, and that would make "fff solves (1)" true by accident.
    • Uniqueness is equality on [0,∞)[0,\infty)[0,∞). The values of ggg off [0,∞)[0,\infty)[0,∞) are free, so a unique-existence quantifier over R→R\mathbb R\to\mathbb RR→R would be false.
    • "Twice continuously differentiable" is read on (0,∞)(0,\infty)(0,∞), since f′′(0+)=−∞f''(0+)=-\inftyf′′(0+)=−∞ (p. 895). At u=0u=0u=0, (1) and (3) are taken in the boundary form sup⁡b[(c−c(b))f′(0)+λ(Ef(−bY)−f(0))]=0\sup_b[(c-c(b))f'(0)+\lambda(\mathbb E f(-bY)-f(0))]=0supb​[(c−c(b))f′(0)+λ(Ef(−bY)−f(0))]=0, with the right derivative f′(0)f'(0)f′(0).
    • λ\lambdaλ and ccc are general. §4 sets λ=c=1\lambda=c=1λ=c=1 by a change of units.
  • Trivialising readings ruled out.

    • Encoding (5) with an unguarded 1/Dg1/D_g1/Dg​ would let a function with a vanishing denominator "solve" (5).
    • Dropping the boundary equation at u=0u=0u=0 from Lemma 3 makes it false: a premium flat near b=0b=0b=0 admits local solutions with b∗≡0b^*\equiv0b∗≡0.
  • Infrastructure needed.

    • existence of the minimiser in DgD_gDg​ and continuity of DgD_gDg​ in xxx;
    • a local existence theorem for singular integral equations in the style of Hipp–Plum;
    • Gronwall's inequality (Mathlib has versions);
    • Banach's fixed-point theorem (ContractingWith).

    The continuation and closure lemmas (milestones 3–5) are reusable for other HJB equations of risk theory with a jump term. Contributions that state and prove intermediate claims of the paper's proofs are welcome as sub-results.

Selected references

  • H. Schmidli, On minimizing the ruin probability by investment and reinsurance, Ann. Appl. Probab. 12(3), 890–907, 2002. https://doi.org/10.1214/aoap/1031863173
  • C. Hipp and M. Plum, Optimal investment for insurers, Insurance Math. Econom. 27(2), 215–228, 2000. https://doi.org/10.1016/S0167-6687(00)00049-4
  • H. Schmidli, Optimal proportional reinsurance policies in a dynamic setting, Scand. Actuar. J. 2001(1), 55–68, 2001. https://doi.org/10.1080/034612301750077338
  • S. N. Ethier and T. G. Kurtz, Markov Processes: Characterization and Convergence, Wiley, 1986. https://doi.org/10.1002/9780470316658
10 thms1 active userReviewed
Markov ChainProbabilityReinforcement Learning+1·Captain: mikedeng1

On Actor-Critic Algorithms: Convergence of Actor-Critic Algorithms with TD(1) and TD(λ) Critics on Finite MDPsResearch Paper

Motivation

Reinforcement-learning methods for Markov decision processes split into critic-only methods, which learn an approximate value function and act greedily with respect to it, and actor-only methods, which adjust a parameterized policy along a simulated estimate of the gradient of its performance. Critic-only methods with function approximation can fail to converge; actor-only gradient estimates have high variance. Actor–critic methods combine the two: a critic learns, by temporal-difference (TD) learning, just enough about the value function to give the actor a low-variance gradient direction.

Konda and Tsitsiklis (SIAM J. Control Optim. 42 (2003)) gave the first convergence proof for actor–critic algorithms whose critic uses linear function approximation. Their key observation, found independently by Sutton, McAllester, Singh and Mansour (NIPS 1999), is that the critic need not approximate the value function: it suffices to compute its projection onto a low-dimensional subspace spanned by features determined by the policy parameterization. Earlier convergence results (Konda and Borkar, 1999) covered lookup-table representations only. Actor–critic algorithms, in many later variants, are now a standard tool of reinforcement learning, and this paper is the reference for their asymptotic analysis in the average-cost setting.

Setting

A finite cost MDP consists of finite sets X\mathbb XX (states) and U\mathbb UU (actions), transition probabilities p(y∣x,u)p(y\mid x,u)p(y∣x,u) and a one-stage cost c(x,u)∈Rc(x,u)\in\mathbb Rc(x,u)∈R. A randomized stationary policy with parameter θ∈Rn\theta\in\mathbb R^nθ∈Rn chooses action uuu in state xxx with probability μθ(u∣x)\mu_\theta(u\mid x)μθ​(u∣x). Under the policy θ\thetaθ, the states {Xk}\{X_k\}{Xk​} form a Markov chain with transition matrix P(θ)xy=∑uμθ(u∣x)p(y∣x,u)P(\theta)_{xy}=\sum_u\mu_\theta(u\mid x)p(y\mid x,u)P(θ)xy​=∑u​μθ​(u∣x)p(y∣x,u), and the pairs {Xk,Uk}\{X_k,U_k\}{Xk​,Uk​} a chain with matrix PθP_\thetaPθ​. When these chains are irreducible and aperiodic, πθ\pi_\thetaπθ​ denotes the stationary law of {Xk}\{X_k\}{Xk​}, ηθ(x,u)=πθ(x)μθ(u∣x)\eta_\theta(x,u)=\pi_\theta(x)\mu_\theta(u\mid x)ηθ​(x,u)=πθ​(x)μθ​(u∣x), and the average cost is

αˉ(θ)=∑x,uc(x,u) ηθ(x,u).\bar\alpha(\theta)=\sum_{x,u}c(x,u)\,\eta_\theta(x,u).αˉ(θ)=x,u∑​c(x,u)ηθ​(x,u).

The score is ψθ(x,u)=∇ln⁡μθ(u∣x)∈Rn\psi_\theta(x,u)=\nabla\ln\mu_\theta(u\mid x)\in\mathbb R^nψθ​(x,u)=∇lnμθ​(u∣x)∈Rn, and ⟨f,g⟩θ=∑x,uηθ(x,u)f(x,u)g(x,u)\langle f,g\rangle_\theta=\sum_{x,u}\eta_\theta(x,u)f(x,u)g(x,u)⟨f,g⟩θ​=∑x,u​ηθ​(x,u)f(x,u)g(x,u).

The critic approximates Q-values by r′ϕθ(x,u)r'\phi_\theta(x,u)r′ϕθ​(x,u) with features ϕθ(x,u)∈Rm\phi_\theta(x,u)\in\mathbb R^mϕθ​(x,u)∈Rm whose span contains the components of ψθ\psi_\thetaψθ​. Along one simulated trajectory (X^k,U^k)(\hat X_k,\hat U_k)(X^k​,U^k​), where U^k+1\hat U_{k+1}U^k+1​ is drawn from μθk(⋅∣X^k+1)\mu_{\theta_k}(\cdot\mid\hat X_{k+1})μθk​​(⋅∣X^k+1​), the algorithm updates an average-cost estimate αk\alpha_kαk​, the critic parameter rkr_krk​ and an eligibility trace Z^k\hat Z_kZ^k​ at step size γk\gamma_kγk​ (equation (3.1)), and the actor parameter by

θk+1=θk−βkΓ(rk) rk′ϕθk(X^k+1,U^k+1) ψθk(X^k+1,U^k+1)(3.2)\theta_{k+1}=\theta_k-\beta_k\Gamma(r_k)\,r_k'\phi_{\theta_k}(\hat X_{k+1},\hat U_{k+1})\,\psi_{\theta_k}(\hat X_{k+1},\hat U_{k+1})\qquad(3.2)θk+1​=θk​−βk​Γ(rk​)rk′​ϕθk​​(X^k+1​,U^k+1​)ψθk​​(X^k+1​,U^k+1​)(3.2)

at a slower step size βk\beta_kβk​. The TD(1) critic resets its trace when the state x∗x^*x∗ is entered; the TD(λ\lambdaλ) critic discounts it by λ∈(0,1)\lambda\in(0,1)λ∈(0,1).

Formalization targets

Goal: Theorem 3.4

Under Assumptions 2.1 (positive, smooth policies; irreducible aperiodic chains; uniform reachability of x∗x^*x∗), 3.1–3.2 (bounded smooth features spanning the scores, uniformly nondegenerate) and 3.3 (two-time-scale step sizes and the scaling Γ\GammaΓ),

(a) TD(1):  lim inf⁡k∣∇αˉ(θk)∣=0  w.p.1;(b) ∀ϵ>0 ∃λˉ<1 ∀λ∈[λˉ,1): lim inf⁡k∣∇αˉ(θk)∣<ϵ  w.p.1.\text{(a) TD(1): }\ \liminf_k|\nabla\bar\alpha(\theta_k)|=0\ \text{ w.p.1};\qquad \text{(b) }\forall\epsilon>0\ \exists\bar\lambda<1\ \forall\lambda\in[\bar\lambda,1):\ \liminf_k|\nabla\bar\alpha(\theta_k)|<\epsilon\ \text{ w.p.1}.(a) TD(1):  kliminf​∣∇αˉ(θk​)∣=0  w.p.1;(b) ∀ϵ>0 ∃λˉ<1 ∀λ∈[λˉ,1): kliminf​∣∇αˉ(θk​)∣<ϵ  w.p.1.

The statement leaves the initial parameters and the initial state–action law arbitrary.

Milestones

In attack order: the gradient formula ∇αˉ(θ)=⟨ψθ,Qθ⟩θ\nabla\bar\alpha(\theta)=\langle\psi_\theta,Q_\theta\rangle_\theta∇αˉ(θ)=⟨ψθ​,Qθ​⟩θ​ for every Poisson solution, with bounded Hessian (Theorem 4.6); boundedness of the regenerative quantities (Lemma 5.1) and of the trace moments (Lemma 5.2); uniform positive definiteness of the critic's mean matrix Gˉ(θ)\bar G(\theta)Gˉ(θ) for TD(1) and TD(λ\lambdaλ) (Lemmas 5.3, 5.6); critic tracking, ∣Gˉ(θk)Rk−hˉ(θk)∣→0|\bar G(\theta_k)R_k-\bar h(\theta_k)|\to0∣Gˉ(θk​)Rk​−hˉ(θk​)∣→0 (Theorem 5.7); the bias of the actor direction, sup⁡θ∣f(θ)−∇αˉ(θ)∣≤C(1−λ)\sup_\theta|f(\theta)-\nabla\bar\alpha(\theta)|\le C(1-\lambda)supθ​∣f(θ)−∇αˉ(θ)∣≤C(1−λ) (Lemma 6.1); the descent inequality (6.1); and the convergence of the noise terms (Lemma 6.2).

Significance

The theorem says that a policy-gradient method whose gradient is estimated by a linearly parameterized TD critic, rather than by Monte Carlo returns, still converges to a stationary point of the average cost (exactly for TD(1), approximately for TD(λ\lambdaλ)). It justifies the compatible features principle (the critic's features must span the score functions), the two-time-scale step-size design, and the trade-off in λ\lambdaλ between variance and bias of the actor direction. The intermediate results are of independent use: Theorem 4.6 is the average-cost policy-gradient theorem for arbitrary Poisson solutions, and Theorem 5.7 is a tracking result for linear stochastic approximation driven by a Markov chain whose kernel changes slowly.

The result is proved in the paper, but the proofs of Theorem 4.6, of parts of the verification of the stochastic-approximation hypotheses and of Theorem 6.3 are given as outlines, with details deferred to a separate appendix and to Theorem A.7, recalled from another paper. No machine-checked proof of the theorem, or of the convergence of any actor–critic method, is known. The mission asks for a formal proof of the finite-case theorem and its milestones, which fills in the outlined steps. The finite-case gradient formula of §2 (Theorem 2.2, recalled from Marbach and Tsitsiklis) is posed, unproved, elsewhere on the platform in a reward-MDP form; it is a special case of milestone Theorem 4.6.

Difficulty

The obvious argument treats the actor as a stochastic gradient method with a noisy gradient estimate. It fails at the first step: the critic's estimate rk′ϕθkr_k'\phi_{\theta_k}rk′​ϕθk​​ is biased at every finite time, and the bias does not vanish unless the critic converges, while the critic's target rˉ(θk)\bar r(\theta_k)rˉ(θk​) moves with the actor. The noise is Markovian, generated by a chain whose transition kernel depends on the current parameter, not a martingale difference sequence. Making the two time scales interact correctly requires a Poisson-equation decomposition of the Markov noise that is uniform in θ\thetaθ, together with moment bounds for the eligibility trace, which for TD(1) is an unbounded sum over a regeneration cycle. Uniformity in θ\thetaθ is essential, because the actor iterates θk\theta_kθk​ are not assumed to stay in a bounded set.

Formalization scope

State and action sets are finite types; parameters live in EuclideanSpace ℝ (Fin n) and EuclideanSpace ℝ (Fin m), so ∣⋅∣|\cdot|∣⋅∣ is the Euclidean norm and Mathlib's gradient is ∇\nabla∇. The transition probability p(y∣x,u)p(y\mid x,u)p(y∣x,u) is stored as p x u y. πθ\pi_\thetaπθ​ is a stationary probability vector of P(θ)P(\theta)P(θ), unique under Assumption 2.1(c). The law of the simulated path is built on (X×U)N(\mathbb X\times\mathbb U)^{\mathbb N}(X×U)N by the Ionescu-Tulcea construction (Kernel.trajMeasure), with the next action drawn under the actor parameter that the recursion computes from the history; nothing about the critic or the actor's behaviour is assumed.

Readings and corrections, each also recorded in the affected items:

  • Assumption 3.3(b) is corrected. The printed ∣r∣Γ(r)∈[C1,C2]|r|\Gamma(r)\in[C_1,C_2]∣r∣Γ(r)∈[C1​,C2​] for all rrr is unsatisfiable at r=0r=0r=0 and would make the theorem vacuous; it is formalized as (1+∣r∣)Γ(r)∈[C1,C2](1+|r|)\Gamma(r)\in[C_1,C_2](1+∣r∣)Γ(r)∈[C1​,C2​], which Γ(r)=1/(1+∣r∣)\Gamma(r)=1/(1+|r|)Γ(r)=1/(1+∣r∣) satisfies. The second inequality of (3.3) is quantified over Rm\mathbb R^mRm, the domain of Γ\GammaΓ, not the printed Rn\mathbb R^nRn.
  • Assumption 4.9 (the features have zero mean under μθ(⋅∣x∗)\mu_\theta(\cdot\mid x^*)μθ​(⋅∣x∗)) is added to part (a), and to the TD(1) cases of Theorem 5.7 and Lemma 6.2: the TD(1) analysis uses it and Theorem 6.3 assumes it, though Theorem 3.4 does not list it.
  • "λ\lambdaλ sufficiently close to 1" means: some λˉ<1\bar\lambda<1λˉ<1 such that all λ∈[λˉ,1)\lambda\in[\bar\lambda,1)λ∈[λˉ,1) work.
  • The α\alphaα-update of (3.1) uses c(X^k+1,U^k+1)c(\hat X_{k+1},\hat U_{k+1})c(X^k+1​,U^k+1​) as printed.
  • In (6.1), Γ(rˉ(θ))\Gamma(\bar r(\theta))Γ(rˉ(θ)) is read as Γ(rˉ(θk))\Gamma(\bar r(\theta_k))Γ(rˉ(θk​)).
  • The matrix ⟨Pθk+1ϕθ,ϕθ′⟩θ\langle P_\theta^{k+1}\phi_\theta,\phi_\theta'\rangle_\theta⟨Pθk+1​ϕθ​,ϕθ′​⟩θ​ in the TD(λ\lambdaλ) Gˉ1\bar G_1Gˉ1​ is oriented so that Gˉ1\bar G_1Gˉ1​ is the steady-state mean of the critic's update matrix.
  • Results stated in the paper for Polish spaces (Theorems 4.6, 5.7) are stated under the finite assumptions that the paper says imply them; "w.p.1" in Theorem 5.7 is made explicit.

The lim inf⁡\liminfliminf is taken in [0,∞][0,\infty][0,∞], so a sequence with ∣∇αˉ(θk)∣→∞|\nabla\bar\alpha(\theta_k)|\to\infty∣∇αˉ(θk​)∣→∞ cannot satisfy the conclusion through a junk value, and differentiability of αˉ\bar\alphaαˉ is part of the conclusion, so the statement cannot hold because Lean's gradient returns 000 at non-differentiable points. Series (tsum) and matrix inverses in the milestone objects converge, respectively are invertible, under the assumptions. The hypotheses are satisfiable: Γ(r)=1/(1+∣r∣)\Gamma(r)=1/(1+|r|)Γ(r)=1/(1+∣r∣), βk=(k+1)−1\beta_k=(k+1)^{-1}βk​=(k+1)−1, γk=(k+1)−2/3\gamma_k=(k+1)^{-2/3}γk​=(k+1)−2/3 and the features ϕθ=ψθ\phi_\theta=\psi_\thetaϕθ​=ψθ​ (which satisfy Assumption 4.9 at every state) meet the parts that could be in doubt.

A complete development needs finite Markov-chain theory uniform in a parameter (existence, uniqueness and smooth dependence of stationary laws; geometric ergodicity; Poisson equations; regeneration), differentiation of θ↦πθ\theta\mapsto\pi_\thetaθ↦πθ​, almost-sure convergence of linear stochastic approximation with Markov noise, and martingale convergence. The Markov-chain and stochastic-approximation parts are reusable well beyond this mission. Contributions of proofs of any milestone, of auxiliary lemmas on parameterized finite Markov chains, and of the general two-time-scale tracking theorem (the paper's Theorem A.7) are welcome.

Selected references

  • V. R. Konda and J. N. Tsitsiklis, On Actor-Critic Algorithms, SIAM J. Control Optim. 42(4), 1143–1166, 2003. https://doi.org/10.1137/S0363012901385691
  • R. S. Sutton, D. McAllester, S. Singh and Y. Mansour, Policy Gradient Methods for Reinforcement Learning with Function Approximation, NIPS 12, 2000. https://papers.nips.cc/paper/1713-policy-gradient-methods-for-reinforcement-learning-with-function-approximation
  • V. R. Konda and V. S. Borkar, Actor-Critic–Type Learning Algorithms for Markov Decision Processes, SIAM J. Control Optim. 38(1), 94–123, 1999. https://doi.org/10.1137/S036301299731669X
  • J. N. Tsitsiklis and B. Van Roy, Average Cost Temporal-Difference Learning, Automatica 35(11), 1799–1808, 1999. https://doi.org/10.1016/S0005-1098(99)00099-0
  • P. Marbach and J. N. Tsitsiklis, Simulation-Based Optimization of Markov Reward Processes, IEEE Trans. Automat. Control 46(2), 191–209, 2001. https://doi.org/10.1109/9.905687
14 thms1 active userReviewed
Operations ResearchProbabilityStochastic Systems·Captain: mikedeng1

Is Network Traffic Approximated by Stable Lévy Motion or Fractional Brownian Motion? 2: Superposed ON/OFF Input Under Slow Growth Converges (fidi) to α-Stable Lévy MotionResearch Paper

Motivation

Measurements of Ethernet and Web traffic in the 1990s showed burstiness at every time scale, long-range dependence, and file sizes and transmission times with tails like x−αx^{-\alpha}x−α, 1<α<21<\alpha<21<α<2 (Leland, Taqqu, Willinger and Wilson, 1994; Crovella and Bestavros, 1997). The standard explanation models each user as an ON/OFF source that alternates between transmitting at unit rate and staying silent, with heavy-tailed period lengths. Many such sources are superposed and time is rescaled. Taqqu, Willinger and Sherman (1997) showed that the limit is fractional Brownian motion when the number of sources is sent to infinity first. That limit is the basis of Gaussian queueing models of network buffers.

Mikosch, Resnick, Rootzén and Stegeman (2002) asked what happens when the number of sources MMM and the time scale TTT grow together. The answer depends on how fast M=M(T)M=M(T)M=M(T) grows. Under fast growth the limit is fractional Brownian motion; this is Theorem 4 of the paper and a separate mission. Under slow growth the limit is an α-stable Lévy motion: it has independent increments and infinite variance, and its sample paths jump. This mission formalizes the slow growth result, Theorem 2. The two answers lead to different buffer and dimensioning models, so the dichotomy matters for traffic engineering.

Setting

Period laws. ON-periods have law FonF_{\mathrm{on}}Fon​ and OFF-periods have law FoffF_{\mathrm{off}}Foff​; both are laws of non-negative random variables. Their means are μon\mu_{\mathrm{on}}μon​ and μoff\mu_{\mathrm{off}}μoff​, and μ=μon+μoff\mu=\mu_{\mathrm{on}}+\mu_{\mathrm{off}}μ=μon​+μoff​. Their right tails Fˉ=1−F\bar F=1-FFˉ=1−F satisfy (2.1)–(2.2):

Fˉon(x)=x−αLon(x),Fˉoff(x)=x−αoffLoff(x),1<α<αoff<2,\bar F_{\mathrm{on}}(x)=x^{-\alpha}L_{\mathrm{on}}(x),\qquad \bar F_{\mathrm{off}}(x)=x^{-\alpha_{\mathrm{off}}}L_{\mathrm{off}}(x),\qquad 1<\alpha<\alpha_{\mathrm{off}}<2,Fˉon​(x)=x−αLon​(x),Fˉoff​(x)=x−αoff​Loff​(x),1<α<αoff​<2,

where LonL_{\mathrm{on}}Lon​ and LoffL_{\mathrm{off}}Loff​ are slowly varying: L(cx)/L(x)→1L(cx)/L(x)\to1L(cx)/L(x)→1 for every c>0c>0c>0. The ON-periods therefore have the heavier tail.

One source. Let X1,X2,…X_1,X_2,\dotsX1​,X2​,… be iid FonF_{\mathrm{on}}Fon​ and Yoff,Y1,Y2,…Y_{\mathrm{off}},Y_1,Y_2,\dotsYoff​,Y1​,Y2​,… iid FoffF_{\mathrm{off}}Foff​. Let BBB be Bernoulli with P(B=1)=μon/μP(B=1)=\mu_{\mathrm{on}}/\muP(B=1)=μon​/μ. Let Xon(0)X^{(0)}_{\mathrm{on}}Xon(0)​ and Yoff(0)Y^{(0)}_{\mathrm{off}}Yoff(0)​ have the integrated-tail laws F(0)(x)=μF−1∫0xFˉ(s) dsF^{(0)}(x)=\mu_F^{-1}\int_0^x\bar F(s)\,dsF(0)(x)=μF−1​∫0x​Fˉ(s)ds. All of these variables are independent. Put Zi=Xi+YiZ_i=X_i+Y_iZi​=Xi​+Yi​. The delay is T0=B(Xon(0)+Yoff)+(1−B)Yoff(0)T_0=B(X^{(0)}_{\mathrm{on}}+Y_{\mathrm{off}})+(1-B)Y^{(0)}_{\mathrm{off}}T0​=B(Xon(0)​+Yoff​)+(1−B)Yoff(0)​, and the renewal epochs are Tn=T0+∑i=1nZiT_n=T_0+\sum_{i=1}^nZ_iTn​=T0​+∑i=1n​Zi​. The source is ON at time ttt when

Wt=B 1[0,Xon(0))(t)+∑n≥01[Tn,Tn+Xn+1)(t)=1.W_t=B\,\mathbf 1_{[0,X^{(0)}_{\mathrm{on}})}(t)+\sum_{n\ge0}\mathbf 1_{[T_n,T_n+X_{n+1})}(t)=1 .Wt​=B1[0,Xon(0)​)​(t)+n≥0∑​1[Tn​,Tn​+Xn+1​)​(t)=1.

This construction makes WWW stationary.

Superposition. MMM iid sources W(1),…,W(M)W^{(1)},\dots,W^{(M)}W(1),…,W(M) feed a server. N(t)=∑mWt(m)N(t)=\sum_mW^{(m)}_tN(t)=∑m​Wt(m)​ is the number of active sources, and A(t)=∫0tN(s) dsA(t)=\int_0^tN(s)\,dsA(t)=∫0t​N(s)ds is the cumulative input; its mean is EA(t)=Mμ−1μontEA(t)=M\mu^{-1}\mu_{\mathrm{on}}tEA(t)=Mμ−1μon​t.

Growth. b(t)=(1/Fˉon)←(t)b(t)=(1/\bar F_{\mathrm{on}})^{\leftarrow}(t)b(t)=(1/Fˉon​)←(t) is the quantile function (2.9). M=M(T)M=M(T)M=M(T) is integer valued, non-decreasing and tends to infinity. Slow Growth Condition 1 is b(MT)/T→0b(MT)/T\to0b(MT)/T→0.

Stable laws. Sα(σ,β,μ)S_\alpha(\sigma,\beta,\mu)Sα​(σ,β,μ) is the law with characteristic function exp⁡{−σα∣θ∣α(1−iβ sign(θ)tan⁡πα2)+iμθ}\exp\{-\sigma^\alpha|\theta|^\alpha(1-i\beta\,\mathrm{sign}(\theta)\tan\frac{\pi\alpha}2)+i\mu\theta\}exp{−σα∣θ∣α(1−iβsign(θ)tan2πα​)+iμθ} for α≠1\alpha\neq1α=1. The α-stable Lévy motion Xα,σ,βX_{\alpha,\sigma,\beta}Xα,σ,β​ has independent stationary increments, and X(t)∼Sα(σt1/α,β,0)X(t)\sim S_\alpha(\sigma t^{1/\alpha},\beta,0)X(t)∼Sα​(σt1/α,β,0).

Formalization targets

Goal: Theorem 2 (p. 40)

With Cα=1−αΓ(2−α)cos⁡(πα/2)C_\alpha=\dfrac{1-\alpha}{\Gamma(2-\alpha)\cos(\pi\alpha/2)}Cα​=Γ(2−α)cos(πα/2)1−α​, σ=Cα−1/α\sigma=C_\alpha^{-1/\alpha}σ=Cα−1/α​ and c=μoff/μ1+1/αc=\mu_{\mathrm{off}}/\mu^{1+1/\alpha}c=μoff​/μ1+1/α, under Condition 1

A(T⋅)−TMμ−1μon(⋅)b(MT)→ fidi c Xα,σ,1(⋅).\frac{A(T\cdot)-TM\mu^{-1}\mu_{\mathrm{on}}(\cdot)}{b(MT)}\xrightarrow{\ fidi\ }c\,X_{\alpha,\sigma,1}(\cdot).b(MT)A(T⋅)−TMμ−1μon​(⋅)​ fidi ​cXα,σ,1​(⋅).

Every constant is stated as printed. The normalisation is the paper's b(MT)b(MT)b(MT) and the limit is the totally skewed (β=1\beta=1β=1) Lévy motion.

Milestones (§5, in the order of the proof)

  • Lemmas 3 and 6: the initial ON-periods (A1A_1A1​) and the overshoots beyond TTT (A3A_3A3​) are negligible at scale b(MT)b(MT)b(MT).
  • Lemmas 4 and 5: the per-source renewal count ξT\xi_TξT​ concentrates around μT=T/μ\mu_T=T/\muμT​=T/μ with probability 1−o(1/M)1-o(1/M)1−o(1/M), plus a truncated-overshoot bound.
  • Lemmas 7, 8 and 9: the left tails of the centred sums Sn=∑k≤nJkS_n=\sum_{k\le n}J_kSn​=∑k≤n​Jk​ are negligible, and replacing ξT\xi_TξT​ by [μT][\mu_T][μT​] is harmless. Here Jk=ron(Xk−μon)−roff(Yk−μoff)J_k=r_{\mathrm{on}}(X_k-\mu_{\mathrm{on}})-r_{\mathrm{off}}(Y_k-\mu_{\mathrm{off}})Jk​=ron​(Xk​−μon​)−roff​(Yk​−μoff​).
  • Lemma 10: [b(MT)]−1A21→cXα,σ,1(1)[b(MT)]^{-1}A_{21}\to cX_{\alpha,\sigma,1}(1)[b(MT)]−1A21​→cXα,σ,1​(1) in distribution.
  • Lemmas 11 and 12: tails of linear combinations of increments, and the two-dimensional limit.

Significance

Theorem 2 identifies the limit regime where the Gaussian heavy-traffic picture fails for ON/OFF traffic. When few sources are observed over long horizons, the aggregate input has infinite-variance, independent-increment fluctuations of size b(MT)≈(MT)1/αb(MT)\approx(MT)^{1/\alpha}b(MT)≈(MT)1/α. Read together with Theorem 4, the result says the growth rate of MMM relative to TTT decides between the two classical models of network input. The proof turns the ON/OFF superposition into row-wise iid random sums and uses a heavy-tailed large-deviation estimate for random walks (Cline and Hsing). That technique carries over to other renewal-reward processes with regularly varying rewards.

The result is proved in the paper. No machine-checked version exists, either of the theorem or of the probabilistic substrate it rests on. Formalization would produce:

  • a stationary alternating renewal process in Lean;
  • a usable notion of regular variation with the quantile function bbb;
  • stable laws given by characteristic functions;
  • the infinitely divisible convergence criterion (Petrov's conditions (A)–(C)) that the proof cites for row-wise iid sums.

Correction. Lemma 12 as printed omits the normalisation [b(MT)]−1[b(MT)]^{-1}[b(MT)]−1 on its left-hand side. Its proof and its use in Theorem 2 both have it, and without it the claim fails because the left-hand side grows. The mission states Lemma 12 with the normalisation. Lemma 11's upper summation limits μTt\mu_{Tt}μTt​ are read as integer parts, as elsewhere in §5.

Difficulty

The natural first idea is to treat A(T)A(T)A(T) as a sum of MMM iid terms and apply the stable central limit theorem for triangular arrays. This fails as stated: each source's contribution A(m)(T)A^{(m)}(T)A(m)(T) is itself a random sum ∑k≤ξTXk\sum_{k\le\xi_T}X_k∑k≤ξT​​Xk​, and its count ξT\xi_TξT​ depends strongly on the XkX_kXk​. Moreover, MMM grows too slowly for a law-of-large-numbers smoothing across sources. The count must be replaced by its mean simultaneously for all MMM sources, with an error probability o(1/M)o(1/M)o(1/M), uniformly as T→∞T\to\inftyT→∞. This requires the two-sided large-deviation estimate of Lemma 4. Its lower tail rests on the Cline–Hsing asymptotics P(Sn>x)∼nFˉ(x)P(S_n>x)\sim n\bar F(x)P(Sn​>x)∼nFˉ(x) for heavy-tailed random walks; its upper tail rests on a Cramér bound. Then Petrov's conditions must be checked at the exact scale b(MT)b(MT)b(MT). That needs Potter bounds for the slowly varying factors and a non-uniform Berry–Esseen estimate for the truncated sums. None of these tools is in Mathlib.

Formalization scope

All declarations are in the namespace NetTraffic.OnOffStable, in one definitions file.

Conventions.

  • Each time scale TTT has its own probability space (ΩT,PT)(\Omega_T,P_T)(ΩT​,PT​), which carries M(T)M(T)M(T) independent sources indexed 0,…,M(T)−10,\dots,M(T)-10,…,M(T)−1. Only laws matter, and the statements quantify over every such family.
  • Lean's X n and Y n are the paper's Xn+1X_{n+1}Xn+1​ and Yn+1Y_{n+1}Yn+1​.
  • ξT\xi_TξT​ is a Set.ncard (junk 000 on a null event). WWW is a tsum of indicators, and AAA is an interval integral.
  • b(t)=inf⁡{x>0:tFˉon(x)≤1}b(t)=\inf\{x>0:t\bar F_{\mathrm{on}}(x)\le1\}b(t)=inf{x>0:tFˉon​(x)≤1}. This set is non-empty for t>0t>0t>0, so bbb is never the junk value 000, and b(MT)/T→0b(MT)/T\to0b(MT)/T→0 is a real condition.
  • Lon(x)=xαFˉon(x)L_{\mathrm{on}}(x)=x^\alpha\bar F_{\mathrm{on}}(x)Lon​(x)=xαFˉon​(x) is assumed slowly varying, which is equivalent to (2.1).
  • Fidi convergence is stated with sorted times: for each kkk there is a probability measure on EuclideanSpace ℝ (Fin k) whose characteristic function is that of c(X(t1),…,X(tk))c(X(t_1),\dots,X(t_k))c(X(t1​),…,X(tk​)), and the laws of the normalised input vectors converge weakly to it. The characteristic function pins the limit down. Its existence is part of the claim, because Mathlib has no stable laws.
  • Convergence in probability means PT(∣YT∣>η)→0P_T(|Y_T|>\eta)\to0PT​(∣YT​∣>η)→0 for every η>0\eta>0η>0.
  • The lemmas that use EA3EA_3EA3​ and EXξT1[⋅]E X_{\xi_T}\mathbf 1[\cdot]EXξT​​1[⋅] also assert integrability, so Bochner junk values cannot trivialise them.

Ruled out. None of the following is acceptable: an encoding in which bbb, NNN or AAA collapses to a junk value; a model hypothesis that no family satisfies (the period laws must be genuine probability laws with finite means, which holds because α>1\alpha>1α>1); a limit "law" not pinned by its characteristic function; or convergence of one-dimensional laws only.

Needed infrastructure.

  • Regular variation: Karamata's theorem and Potter bounds (Proposition 2, App. B).
  • Large deviations for heavy-tailed random walks (Corollary 1, App. A).
  • Renewal theory for delayed alternating renewal processes.
  • Stable laws and the convergence criterion for row-wise iid sums.

The regular-variation and stable-law layers are reusable well beyond this mission. Formalizations of any milestone, of those general tools, or of alternative proofs of the one-dimensional limit are welcome.

Selected references

  • T. Mikosch, S. Resnick, H. Rootzén and A. Stegeman, Is network traffic approximated by stable Lévy motion or fractional Brownian motion?, Ann. Appl. Probab. 12(1):23–68, 2002. https://doi.org/10.1214/aoap/1015961155
  • M. S. Taqqu, W. Willinger and R. Sherman, Proof of a fundamental result in self-similar traffic modeling, Computer Communication Review 27:5–23, 1997. https://doi.org/10.1145/251007.251012
  • W. E. Leland, M. S. Taqqu, W. Willinger and D. V. Wilson, On the self-similar nature of Ethernet traffic (extended version), IEEE/ACM Trans. Networking 2(1):1–15, 1994. https://doi.org/10.1109/90.282603
  • M. E. Crovella and A. Bestavros, Self-similarity in World Wide Web traffic: evidence and possible causes, IEEE/ACM Trans. Networking 5(6):835–846, 1997. https://doi.org/10.1109/90.650143
  • D. B. H. Cline and T. Hsing, Large deviation probabilities for sums of random variables with heavy or subexponential tails, Technical report, Texas A&M University, 1991.
  • G. Samorodnitsky and M. S. Taqqu, Stable Non-Gaussian Random Processes, Chapman and Hall, 1994. https://doi.org/10.1201/9780203738818
13 thms1 active userReviewed
Operations ResearchProbabilityStochastic Systems·Captain: mikedeng1

Is Network Traffic Approximated by Stable Lévy Motion or Fractional Brownian Motion? 3: Infinite Source Poisson Input Under Fast Growth Converges in (D[0,∞), J₁) to Fractional Brownian MotionResearch Paper

Why heavy-tailed input matters

Measurements of Ethernet and Internet traffic in the 1990s showed that cumulative traffic is self-similar and long-range dependent: correlations of the input rate decay so slowly that they are not summable, and fluctuations at large time scales do not average out the way Poisson-type models predict. A widely accepted explanation is that the lengths of individual transmissions (file sizes, session durations) are heavy tailed, with infinite variance. Queueing and capacity-planning calculations then depend on which stochastic process approximates the cumulative input over long horizons.

Two candidate approximations had been proposed: fractional Brownian motion, a Gaussian self-similar process with dependent increments (Taqqu, Willinger and Sherman, 1997; Leland, Taqqu, Willinger and Wilson, 1994), and α-stable Lévy motion, a heavy-tailed process with independent increments. Mikosch, Resnick, Rootzén and Stegeman (Ann. Appl. Probab. 12 (2002) 23–68) showed that both arise from the same model, and that the answer depends on how fast the connection rate grows relative to the time scale. This mission formalizes their Gaussian answer for the infinite source Poisson model: Theorem 3.

  • 1994–1997: Leland et al. measure self-similarity in Ethernet traffic; Willinger, Taqqu, Sherman and Wilson derive fractional Brownian motion from ON/OFF sources with heavy-tailed periods, in an iterated limit (first the number of sources, then time).
  • 2002: Mikosch, Resnick, Rootzén and Stegeman take both limits simultaneously and identify a slow-growth regime (stable Lévy limit) and a fast-growth regime (fractional Brownian limit), for both the ON/OFF and the infinite source Poisson model.

The infinite source Poisson model

Connections start at the points (Γk)k∈Z(\Gamma_k)_{k\in\mathbb Z}(Γk​)k∈Z​ of a homogeneous Poisson process on R\mathbb RR with rate λ\lambdaλ, labelled so that Γ0<0<Γ1\Gamma_0<0<\Gamma_1Γ0​<0<Γ1​; equivalently, −Γ0-\Gamma_0−Γ0​, Γ1\Gamma_1Γ1​ and Γk+1−Γk\Gamma_{k+1}-\Gamma_kΓk+1​−Γk​ (k≠0k\neq0k=0) are iid exponential with parameter λ\lambdaλ. Connection kkk transmits at unit rate for a length XkX_kXk​; the XkX_kXk​ are iid with law FonF_{\mathrm{on}}Fon​ on [0,∞)[0,\infty)[0,∞) and independent of (Γk)(\Gamma_k)(Γk​). The tail Fˉon(x)=P(Xk>x)\bar F_{\mathrm{on}}(x)=P(X_k>x)Fˉon​(x)=P(Xk​>x) is regularly varying:

Fˉon(x)=x−αL(x),x>0,1<α<2,(2.8)\bar F_{\mathrm{on}}(x)=x^{-\alpha}L(x),\qquad x>0,\quad 1<\alpha<2,\tag{2.8}Fˉon​(x)=x−αL(x),x>0,1<α<2,(2.8)

with LLL slowly varying (L(cx)/L(x)→1L(cx)/L(x)\to1L(cx)/L(x)→1 for every c>0c>0c>0). The mean μon\mu_{\mathrm{on}}μon​ is finite and the variance infinite. The number of active connections and the cumulative input are

N(t)=∑k1[Γk≤t<Γk+Xk],A(t)=∫0tN(s) ds.N(t)=\sum_k\mathbf 1[\Gamma_k\le t<\Gamma_k+X_k],\qquad A(t)=\int_0^tN(s)\,ds .N(t)=k∑​1[Γk​≤t<Γk​+Xk​],A(t)=∫0t​N(s)ds.

The model is indexed by a time scale T→∞T\to\inftyT→∞, with a connection rate λ=λ(T)\lambda=\lambda(T)λ=λ(T) that is non-decreasing in TTT. With the quantile function b(t)=(1/Fˉon)←(t)=inf⁡{x>0:1/Fˉon(x)≥t}b(t)=(1/\bar F_{\mathrm{on}})^{\leftarrow}(t)=\inf\{x>0:1/\bar F_{\mathrm{on}}(x)\ge t\}b(t)=(1/Fˉon​)←(t)=inf{x>0:1/Fˉon​(x)≥t}, the fast growth Condition 2 is

lim⁡T→∞b(λT)T=∞,\lim_{T\to\infty}\frac{b(\lambda T)}{T}=\infty ,T→∞lim​Tb(λT)​=∞,

equivalently λTFˉon(T)→∞\lambda T\bar F_{\mathrm{on}}(T)\to\inftyλTFˉon​(T)→∞: many transmissions longer than the observation window start in it. With σT2(1)=λT3Fˉon(T)\sigma_T^2(1)=\lambda T^3\bar F_{\mathrm{on}}(T)σT2​(1)=λT3Fˉon​(T) the normalised input is

GT(t)=A(Tt)−λμonTt[σT2(1) σ2]1/2,t≥0.G_T(t)=\frac{A(Tt)-\lambda\mu_{\mathrm{on}}Tt}{[\sigma_T^2(1)\,\sigma^2]^{1/2}},\qquad t\ge0 .GT​(t)=[σT2​(1)σ2]1/2A(Tt)−λμon​Tt​,t≥0.

Standard fractional Brownian motion BHB_HBH​ with Hurst index H∈(0,1)H\in(0,1)H∈(0,1) is a mean-zero Gaussian process on [0,∞)[0,\infty)[0,∞) with a.s. continuous paths and Cov(BH(t),BH(s))=12(t2H+s2H−∣t−s∣2H)\mathrm{Cov}(B_H(t),B_H(s))=\tfrac12\big(t^{2H}+s^{2H}-|t-s|^{2H}\big)Cov(BH​(t),BH​(s))=21​(t2H+s2H−∣t−s∣2H).

Formalization targets

Goal: Theorem 3 (corrected)

Under (2.8), λ\lambdaλ non-decreasing and Condition 2,

GT(⋅)→dBH(⋅)in (D[0,∞),J1),H=3−α2,σ2=2(α−1)(2−α)(3−α).G_T(\cdot)\xrightarrow{d}B_H(\cdot)\quad\text{in }(\mathbb D[0,\infty),J_1),\qquad H=\frac{3-\alpha}2,\qquad \sigma^2=\frac{2}{(\alpha-1)(2-\alpha)(3-\alpha)} .GT​(⋅)d​BH​(⋅)in (D[0,∞),J1​),H=23−α​,σ2=(α−1)(2−α)(3−α)2​.

On the constant. The paper takes σ2\sigma^2σ2 from its display (6.6), σ2=13−α[α2−α+2μon]\sigma^2=\frac1{3-\alpha}\big[\frac{\alpha}{2-\alpha}+\frac2{\mu_{\mathrm{on}}}\big]σ2=3−α1​[2−αα​+μon​2​]. With that value the theorem is false. Two terms are lost in the variance bookkeeping of the proof: (6.4) omits the factor λm2∼λm3∼λμon\lambda m_2\sim\lambda m_3\sim\lambda\mu_{\mathrm{on}}λm2​∼λm3​∼λμon​ in the variances of the second and third regional sums, each of which contributes 1/(3−α)1/(3-\alpha)1/(3−α), and (6.2) discards (P4−EP4)T(P_4-EP_4)T(P4​−EP4​)T by an argument valid only under slow growth; under Condition 2 its variance is ∼λT3Fˉon(T)/(α−1)\sim\lambda T^3\bar F_{\mathrm{on}}(T)/(\alpha-1)∼λT3Fˉon​(T)/(α−1). The sum α(2−α)(3−α)+23−α+1α−1\frac{\alpha}{(2-\alpha)(3-\alpha)}+\frac2{3-\alpha}+\frac1{\alpha-1}(2−α)(3−α)α​+3−α2​+α−11​ equals 2(α−1)(2−α)(3−α)\frac2{(\alpha-1)(2-\alpha)(3-\alpha)}(α−1)(2−α)(3−α)2​, which is also what VarA(T)=2∫0T(T−h) λ∫h∞Fˉon(v) dv dh\mathrm{Var}A(T)=2\int_0^T(T-h)\,\lambda\int_h^\infty\bar F_{\mathrm{on}}(v)\,dv\,dhVarA(T)=2∫0T​(T−h)λ∫h∞​Fˉon​(v)dvdh and Karamata's theorem give. Since VarBH(1)=1\mathrm{Var}B_H(1)=1VarBH​(1)=1, only this value makes the limit standard. All other constants are those of the paper.

Intermediate targets

The milestones follow the proof: Lemma 1 (Condition 2 ⇔ λTFˉon(T)→∞\lambda T\bar F_{\mathrm{on}}(T)\to\inftyλTFˉon​(T)→∞ ⇔ Cov(NT(0),NT(T))→∞\mathrm{Cov}(N_T(0),N_T(T))\to\inftyCov(NT​(0),NT​(T))→∞), Lemma 2 (fast part), the covariance identity (2.14), the moment asymptotics (4.9) and (4.14), the central limit theorem (6.3) for the first regional sum, the one-dimensional limit (6.5) with the corrected variance, stationarity of the increments of GTG_TGT​, and the fourth-moment bounds (6.8) and E(GT(t+u)−GT(t))4≤cu2E(G_T(t+u)-G_T(t))^4\le cu^2E(GT​(t+u)−GT​(t))4≤cu2 that give tightness.

Significance

The theorem identifies the fast-growth regime of the infinite source Poisson model (the M/G/∞ input model) with fractional Brownian motion, so that Gaussian long-range-dependent models of network traffic are limits of a mechanistic model, not postulates; together with Theorem 1 it shows that the choice between Gaussian and stable approximations is decided by a single growth condition, b(λT)/T→0b(\lambda T)/T\to0b(λT)/T→0 or →∞\to\infty→∞. Queueing analyses of fluid queues fed by fractional Brownian motion inherit their justification from limits of this kind.

The result is proved in the paper, apart from the constant discussed above; it is not formalized anywhere. A formalization adds a machine-checked statement with the correct normalisation and a reusable development of regularly varying tails, Poisson marked point processes on R×[0,∞)\mathbb R\times[0,\infty)R×[0,∞), fractional Brownian motion, and Skorokhod's J1J_1J1​ topology on D[0,∞)\mathbb D[0,\infty)D[0,∞).

Difficulty

The obvious argument is a central limit theorem for A(T)A(T)A(T), but A(T)A(T)A(T) is not a sum of independent terms of bounded variance: transmissions that straddle the window are long and heavy tailed, and their contribution is of the same order as the bulk. The proof splits the points into four regions according to where a transmission starts and ends, and each region needs its own moment asymptotics through Karamata's theorem; the bookkeeping is exactly where the printed constant goes wrong. The functional statement then needs tightness in D[0,∞)\mathbb D[0,\infty)D[0,∞): a fourth-moment bound on increments that is uniform in TTT and in the position of the increment, which requires Potter-type bounds on Fˉon(uT)/Fˉon(T)\bar F_{\mathrm{on}}(uT)/\bar F_{\mathrm{on}}(T)Fˉon​(uT)/Fˉon​(T) for small uuu. Convergence of finite-dimensional distributions alone does not give the theorem.

Formalization scope

  • Time and models. T→∞T\to\inftyT→∞ along atTop on R\mathbb RR. Each model TTT has its own probability space; the theorems hold for every family of models with the stated rate and length law. Time in GTG_TGT​ runs over R≥0\mathbb R_{\ge0}R≥0​.
  • Standing hypotheses. FonF_{\mathrm{on}}Fon​ is a probability measure with Fon((−∞,0))=0F_{\mathrm{on}}((-\infty,0))=0Fon​((−∞,0))=0 (lengths are non-negative, implicit on the page); (2.8) is stated as "x↦xαFˉon(x)x\mapsto x^\alpha\bar F_{\mathrm{on}}(x)x↦xαFˉon​(x) is slowly varying", 1<α<21<\alpha<21<α<2; λ(T)>0\lambda(T)>0λ(T)>0 and λ\lambdaλ is non-decreasing (§3.1); Condition 2. No other hypothesis is added.
  • Junk values ruled out. bbb is an infimum over {x>0:tFˉon(x)≤1}\{x>0:t\bar F_{\mathrm{on}}(x)\le1\}{x>0:tFˉon​(x)≤1}, never a division by zero; NNN is a cardinality and A1A_1A1​ a sum of non-negative terms, whose junk values (000 on an infinite set) occur only on null events; fourth moments are asserted together with integrability; equalities of laws are asserted together with measurability. The limit is pinned completely: Gaussian, mean zero, the covariance above with σH=1\sigma_H=1σH​=1, H=(3−α)/2H=(3-\alpha)/2H=(3−α)/2, continuous paths. Fidi convergence in place of the functional limit, a free scale in the limit, or a limit identified only by its marginals would each be a different, weaker theorem.
  • Weak convergence is stated in coupling form: for every sequence Tn→∞T_n\to\inftyTn​→∞ there is one probability space with copies YnY_nYn​ of the laws of GTnG_{T_n}GTn​​ (all finite-dimensional distributions) and a standard fractional Brownian motion Y′Y'Y′ such that Yn→Y′Y_n\to Y'Yn​→Y′ in J1J_1J1​ almost surely, càdlàg paths included. On the Polish space D[0,∞)\mathbb D[0,\infty)D[0,∞) this is equivalent to weak convergence. Because A(T⋅)A(T\cdot)A(T⋅) and BHB_HBH​ have continuous paths, J1J_1J1​ convergence here coincides with locally uniform convergence.
  • Infrastructure needed: Karamata's theorem and Potter bounds for regularly varying functions; the Poisson random measure of the marked points and its restriction to disjoint regions; a Lyapunov central limit theorem for Poisson sums; fractional Brownian motion and its existence; Billingsley's moment criterion for tightness in D[0,K]\mathbb D[0,K]D[0,K]. The regular-variation and Skorokhod-space material is reusable well beyond this mission; contributions of any of these pieces, and of any milestone, are welcome.

Selected references

  • T. Mikosch, S. Resnick, H. Rootzén and A. Stegeman, Is network traffic approximated by stable Lévy motion or fractional Brownian motion?, Ann. Appl. Probab. 12(1) (2002), 23–68. https://doi.org/10.1214/aoap/1015961155
  • W. E. Leland, M. S. Taqqu, W. Willinger and D. V. Wilson, On the self-similar nature of Ethernet traffic (extended version), IEEE/ACM Trans. Networking 2 (1994), 1–15. https://doi.org/10.1109/90.282603
  • W. Willinger, M. S. Taqqu, R. Sherman and D. V. Wilson, Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level, IEEE/ACM Trans. Networking 5 (1997), 71–86. https://doi.org/10.1109/90.554723
  • N. H. Bingham, C. M. Goldie and J. L. Teugels, Regular Variation, Cambridge University Press, 1987. https://doi.org/10.1017/CBO9780511721434
  • P. Billingsley, Convergence of Probability Measures, Wiley, 1968 (Theorem 12.3, moment criterion for tightness).
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