The mathematical foundations of computation: which problems can be solved, by what algorithms, and at what cost in time, space, or communication. Distinguished by its emphasis on rigor and unconditional lower bounds, it spans computational complexity, algorithm design, automata and computability, cryptography, and the analysis of Boolean functions.
Missions
🏆Completed
Captain: marwahaha
Coppersmith–Winograd Bound: omega < 2.376Research Paper
AI generated but i think correct. I think the milestones make it really annoying but the central theorem looks correct.
## Motivation
The **matrix-multiplication exponent** measures the asymptotic number of field operations needed to multiply two square matrices. A bound $\omega<c$ means that, for every $\varepsilon>0$, two $n\times n$ matrices can be multiplied using $O(n^{c+\varepsilon})$ arithmetic operations. Matrix multiplication is a central benchmark in algebraic complexity and a primitive for many algorithms in linear algebra, graph theory, and symbolic computation.
After Strassen showed that $\omega<3$, a sequence of tensor constructions reduced the exponent further. Schönhage's asymptotic sum inequality made it possible to exploit simultaneous matrix products rather than a single square product. In 1990, [Don Coppersmith and Shmuel Winograd](https://www.sciencedirect.com/science/article/pii/S0747717108800132) combined an explicit low-border-rank tensor with a block extraction argument based on Salem--Spencer sets. Their basic analysis gave $\omega<2.38719$; coupling the random weights in the tensor square sharpened this to $\omega<2.375477$, hence the exact rational consequence $\omega<2.376$.
This mission formalizes that historical Coppersmith--Winograd result. It follows the source tensor and its actual block restrictions, while excluding placeholder “laser values” that are not backed by extracted direct sums of matrix-multiplication tensors.
## Setting
For a field $K$, an order-three tensor is represented by three finite-dimensional $K$-vector spaces and an element of their tensor product. The matrix-multiplication tensor
$$
\langle a,b,c\rangle_K
=\sum_{i<a}\sum_{j<b}\sum_{k<c}
x_{ij}\otimes y_{jk}\otimes z_{ki}
$$
encodes multiplication of an $a\times b$ matrix by a $b\times c$ matrix. A **restriction** applies one linear map to each tensor leg. A **degeneration** permits those maps to depend polynomially on a formal parameter and selects their first nonzero coefficient. Thus a degeneration from the diagonal tensor $I_r$ is a border-rank certificate $\underline R(T)\le r$.
The **Coppersmith--Winograd tensor** with parameter $q$ is
$$
T_q=
\sum_{i=1}^{q}
(x_0y_i z_i+x_i y_0z_i+x_i y_i z_0)
+x_0y_0z_{q+1}+x_0y_{q+1}z_0+x_{q+1}y_0z_0.
$$
It has border rank at most $q+2$. Its coordinates carry three classes, indexed by $0,1,2$, and its six nonzero block types are
$$
(0,1,1),\ (1,0,1),\ (1,1,0),\ (0,0,2),\ (0,2,0),\ (2,0,0).
$$
The first three blocks are matrix-multiplication tensors with dimensions $(1,1,q)$, $(q,1,1)$, and $(1,q,1)$; the other three are scalar products. Tensor powers therefore contain many typed rectangular matrix products. The **laser method** selects a large family with disjoint coordinate blocks and applies Schönhage's asymptotic sum inequality to all surviving products simultaneously.
## Formalization targets
### Goal: the 1990 Coppersmith--Winograd bound
For every field $K$,
$$
\operatorname{matMulExp}(K)<\frac{297}{125}=2.376.
$$
The Lean goal has the same quantified proposition and the same `matMulExp` definition as the existing Schönhage-bound mission; only the theorem identifier and rational endpoint change.
### Tensor and block foundations
The development records the characteristic-free order-three degeneration
$$
T_q\unlhd I_{q+2}
$$
and the exact matrix-product dimensions associated with every supported type sequence in $T_q^{\otimes N}$. These statements identify the algebraic input before any asymptotic counting is used.
### Coupled-weight extraction
For $q=6$, the tensor-square grading and the coupled-weight pruning must produce the direct sums and asymptotic inequality stated in Section 8 and in the coupled-constituent lemma on journal pp. 270--272. The final numerical milestone certifies the rational endpoint $297/125$ from exact inequalities, rather than treating the decimal $2.375477$ as a proof object.
## Significance
The result was the strongest matrix-multiplication bound for roughly two decades and introduced the tensor family that underlies the classical laser-method line of work. A formal proof supplies a checked bridge from an explicit border-rank identity to an exponent bound whose combinatorial extraction is substantially more delicate than the earlier Schönhage examples.
The formalization also produces reusable infrastructure. The order-three CW degeneration is an explicit polynomial-family test case over arbitrary fields. The six block identifications and type-count formulas can be reused in analyses of tensor powers. A faithful extraction predicate, stated through actual restrictions to direct sums of `MMObj` tensors, separates sound laser arguments from formulas that count incompatible or coordinate-sharing blocks as independent.
The mathematical bound is known. The open work is its machine-checked reconstruction in Lean. The border-rank theorem, per-type matrix-product restriction layer, tensor-square support invariant, balanced block calculation, Salem--Spencer set theorem, and exact $q=6$ numerical endpoint are already proved. The unrestricted value/rank bridge, the coupled-constituent extraction, and the full Section 8 auxiliary inequality remain the substantive frontier.
## Difficulty
The main difficulty is not expanding $T_q$ or evaluating a decimal logarithm. A tensor power contains exponentially many typed terms, but most share variables. They cannot all be placed in a direct sum, and counting all joint type sequences overestimates the usable matrix products. The source hashes coordinate blocks into a large progression-free set and prunes collisions so that the surviving blocks are genuinely independent.
The $2.376$ improvement adds a second layer. It begins with $T_q^{\otimes2}$, regroups variables into five classes, couples weights that were independent in the simpler analysis, and estimates a nontrivial central block by a further extraction. A formal proof must track the direction of every restriction, the exact multiplicities of all block types, and the loss introduced by pruning. Replacing exponential surviving-block counts by a polynomial number of blocks, or using joint entropy without the marginal compatibility constraints, changes the mathematical claim and is outside the mission.
## Formalization scope
The mission uses the existing `TensorObj`, `MMObj`, `TensorObj.Restrict`, `Degenerates`, `tensorAsymptoticRank`, `matMulExp`, and `matMulExp_strassen` declarations in the Mathlib environment pinned by the earlier matrix-multiplication mission. Tensor dimensions and type counts are natural numbers; exponent and optimization inequalities are real-valued. All top-level bounds quantify over an arbitrary field, matching the integral polynomial identities used by the construction.
Laser statements must exhibit, directly or through a faithful reusable predicate, restrictions from a tensor power to a finite direct sum of concrete matrix-multiplication tensors. The number and dimensions of the summands remain part of the witness. A constant-valued “laser functional,” a vacuous witness hypothesis, or a capacity definition that discards the exponential number of surviving blocks does not satisfy the mission.
Welcome contributions include restriction composition lemmas, tensor-power block equivalences, multinomial and entropy estimates with all marginal constraints, formal Salem--Spencer pruning, exact real-inequality certificates, and the coupled central-block value lemma. Every milestone should cite the corresponding equation, table, or lemma in the primary paper.
## Selected references
- Don Coppersmith and Shmuel Winograd, *Matrix Multiplication via Arithmetic Progressions*, Journal of Symbolic Computation 9, 1990, pp. 251--280. [ScienceDirect](https://www.sciencedirect.com/science/article/pii/S0747717108800132).
- Arnold Schönhage, *Partial and Total Matrix Multiplication*, SIAM Journal on Computing 10(3), 1981, pp. 434--455. [DOI 10.1137/0210032](https://doi.org/10.1137/0210032).
- Avi Wigderson and Jeroen Zuiddam, *Asymptotic Spectra: Theory, Applications and Extensions*, 2023, for the tensor restriction and asymptotic-rank framework used by the Lean development. [Author manuscript](https://www.math.ias.edu/~avi/PUBLICATIONS/WigdersonZu_Final_Draft_Oct2023.pdf).
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Captain: marwahaha
Schönhage–Pan–Winograd Bound: omega < 2.522Research Paper
## Motivation
The **matrix-multiplication exponent** measures the asymptotic arithmetic cost of multiplying square matrices. An upper bound $\omega<c$ means that, over the field under consideration, $N\times N$ matrices can be multiplied using $O(N^{c+\varepsilon})$ arithmetic operations for every $\varepsilon>0$. Improvements to $\omega$ are a central benchmark in algebraic complexity because matrix multiplication is also a basic subroutine in linear algebra, graph algorithms, and symbolic computation.
The existing Prove2Me mission formalizes Schönhage's bound $\omega<2.55$ from a concrete two-summand tensor degeneration. The present mission advances the same formal development to the next clean historical construction. Pan and Winograd found a simultaneous approximate algorithm for three matrix products; [Romani recorded its tensor form and the parameter choice $n=11$, $k=5$](https://iris.cnr.it/retrieve/7f08fe3e-3ef4-42b1-b84f-82ba5e09c61a/prod_421763-doc_149822.pdf), which gives $\omega\le 2.5218127\ldots$. [Schönhage's 1981 paper](https://doi.org/10.1137/0210032) reports the equivalent bound $3\log 52/\log 110$ in the arbitrary-field setting. The exact formal target here is the slightly weaker rational inequality $\omega<1261/500=2.522$.
## Setting
For a field $K$, the **matrix-multiplication tensor** $\langle a,b,c\rangle_K$ encodes multiplication of an $a\times b$ matrix by a $b\times c$ matrix:
$$
\langle a,b,c\rangle_K
=\sum_{i<a}\sum_{j<b}\sum_{\ell<c}
e_{ij}\otimes e_{j\ell}\otimes e_{\ell i}.
$$
A direct sum places several such tensors in disjoint coordinate blocks. A tensor $T$ has border rank at most $r$ when it is a polynomial degeneration of the diagonal tensor $I_r=\sum_{s<r}e_s\otimes e_s\otimes e_s$. In the Lean development this relation is `Degenerates T (TensorObj.diagObj K 3 r)`. The argument order matters: the first tensor is the target and the diagonal tensor is the source.
The platform already defines ordinary tensor rank, asymptotic tensor rank, the tensor-rank exponent `matMulExp K`, the equivalent Strassen-preorder exponent `matMulExp_strassen K`, and Schönhage's asymptotic sum inequality. This mission reuses those declarations. No alternative definition of $\omega$ is introduced.
## Formalization targets
The goal has exactly the same quantified proposition as the existing $2.55$ mission, with only the rational endpoint changed:
$$
\forall K\;[\mathrm{Field}(K)],\qquad
\operatorname{matMulExp}(K)<\frac{1261}{500}.
$$
The source construction to be formalized is
$$
\underline R\!\left(
\langle1,5,22\rangle_K\oplus
\langle11,2,5\rangle_K\oplus
\langle10,11,1\rangle_K
\right)\le156.
$$
Each summand has volume $110$:
$$
1\cdot5\cdot22=11\cdot2\cdot5=10\cdot11\cdot1=110.
$$
The milestone chain records the degeneration, its asymptotic-rank consequence, the exact numerical implication
$$
3\cdot110^{\omega^{\mathrm{Str}}_K/3}\le156
\quad\Longrightarrow\quad
\omega^{\mathrm{Str}}_K<\frac{1261}{500},
$$
and the resulting Strassen-form exponent bound. The public goal then transfers the bound to `matMulExp K` through the already established equality of the two exponent definitions.
## Significance
Mathematically, this construction improves the concrete exponent certified by the existing mission from $2.55$ to $2.522$ without changing the surrounding theory. It isolates the first genuinely new ingredient after the accepted Schönhage example: a larger simultaneous tensor degeneration rather than a sharper numerical estimate for the old witness.
For formalization, the mission tests whether the current polynomial-degeneration API can express a historically important trilinear aggregation at realistic scale. Once the explicit witness is available, the remaining declarations form a reusable template for later bounds: a source tensor degeneration, an asymptotic-rank bound, a specialization of the asymptotic sum inequality, and a final exponent transfer. This creates a trustworthy stepping stone toward the Coppersmith--Winograd tensor and later laser-method analyses.
The $2.522$ theorem is known mathematically; the open work is its machine-checked Lean formalization. The exact numerical endpoint and every downstream bridge from the degeneration have already been checked locally. The explicit Pan--Winograd degeneration remains the substantive open milestone.
## Difficulty
The central difficulty is not the logarithmic comparison. It is constructing and verifying the polynomial family whose leading nonzero coefficient is exactly the tagged direct sum of the three matrix-multiplication tensors and whose earlier coefficients vanish. The family has $156$ diagonal source slots and many indexed target coordinates. A proof must account for all mixed-coordinate terms and all cancellations uniformly over an arbitrary field.
[Romani's published summary](https://iris.cnr.it/retrieve/7f08fe3e-3ef4-42b1-b84f-82ba5e09c61a/prod_421763-doc_149822.pdf) states the approximate-rank inequality but does not spell out a Lean-ready map between its trilinear forms and the platform's `TensorObj.bigAdd` coordinate spaces. A solver must therefore recover the source indexing carefully and prove that the resulting modewise linear maps have the required coefficients. Reversing the degeneration direction, conflating tensor rank with asymptotic rank, or silently assuming a characteristic-zero scalar identity would invalidate the result.
## Formalization scope
All theorems quantify over an arbitrary type $K$ with `[Field K]`, matching the existing Schönhage goal and the arbitrary-field statement of the source bound. Tensor spaces are finite-dimensional function spaces already packaged by `MMObj`; the three products are combined with `TensorObj.bigAdd`. Border rank is represented by the existing finitely supported polynomial-family predicate `Degenerates`. Because the source summary specifies approximate rank but not a leading order, the main degeneration milestone existentially quantifies that order instead of hard-coding one.
The mission includes no placeholder laser-value definition and makes no claim about the later $2.376$ analysis. It also excludes Schönhage's additional microscopic symmetrization improvement beyond $3\log52/\log110$. A valid solution must construct the stated degeneration itself; a vacuous hypothesis or a redefinition of `matMulExp` is outside scope.
Reusable contributions include coefficient lemmas for polynomial tensor families, finite-index equivalences for direct sums, and generic aggregation identities that specialize to the $n=11$, $k=5$ witness. Contributions that merely restate the target under stronger field hypotheses do not close the arbitrary-field milestone.
## Selected references
- A. Schönhage, *Partial and Total Matrix Multiplication*, SIAM Journal on Computing 10(3), 1981, pp. 434--455. [DOI 10.1137/0210032](https://doi.org/10.1137/0210032).
- Francesco Romani, *Some Properties of Disjoint Sums of Tensors Related to Matrix Multiplication*, CNR Nota Interna B80-4, February 1980, printed p. 6; journal version, SIAM Journal on Computing 11(2), 1982. [Archived preprint](https://iris.cnr.it/retrieve/7f08fe3e-3ef4-42b1-b84f-82ba5e09c61a/prod_421763-doc_149822.pdf) and [DOI 10.1137/0211020](https://doi.org/10.1137/0211020).
- Avi Wigderson and Jeroen Zuiddam, *Asymptotic Spectra: Theory, Applications and Extensions*, 2023, for the tensor-preorder and asymptotic-rank framework reused by the Lean development. [Author manuscript](https://www.math.ias.edu/~avi/PUBLICATIONS/WigdersonZu_Final_Draft_Oct2023.pdf).
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Captain: wurtle
Generalization of Hinging PlanesResearch Paper
A continuous piecewise linear (CPWL) function is one assembled from finitely many flat pieces glued along flat seams. Every ReLU network computes such a function, and every such function is computed by some ReLU network. Questions about how deep a network must be are therefore questions about the internal structure of CPWL functions.
In 1993 Breiman built such functions from hinges: maxima of two affine maps. Sums of hinges approximate anything, but from two dimensions up they fail to represent most CPWL functions exactly. Wang and Sun (2005) widened the maxima, proving that every CPWL function on ℝⁿ is a signed sum of maxima of at most n+1 affine maps. Twenty years on it remains the workhorse structural fact, reducing any question about a network to a question about a single max gate and underpinning every known upper bound on the depth of exact representation.
That includes the newest one: at STOC 2026, Bakaev et al disproved the short standing conjecture that ⌈log₂(n+1)⌉ hidden layers are necessary, showing ⌈log₃(n−1)⌉+1 suffice. In this mission we deliver a machine-checked proof of the Wang and Sun theorem so future formalizations of network expressivity can invoke it rather than reprove it. Note that we take as given the lattice representation of Tarela and Martínez, independently proved by Ovchinnikov, which writes any CPWL function as a max of mins of its affine pieces. That is the one external ingredient the argument consumes, and our definition of CPWL builds it in.
3 thms3 active users
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Captain: joe
The Sipser–Gács–Lautemann TheoremResearch Paper
Randomness appears to enlarge efficient computation, but the Sipser–Gács–Lautemann theorem places every bounded-error probabilistic polynomial-time language at the second level of the polynomial hierarchy, giving one of complexity theory’s foundational limits on the power of randomization.
55 thms3 active usersReviewed
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Captain: marwahaha
Asymmetric Hashing Square Bound: omega < 2.3747Research Paper
AI generated, I think it's correct
## Motivation
The **matrix-multiplication exponent** measures the asymptotic arithmetic cost of multiplying square matrices. A bound $\omega<c$ means that, over the field under consideration, $n\times n$ matrices can be multiplied in $O(n^{c+\varepsilon})$ field operations for every $\varepsilon>0$. Matrix multiplication is a central benchmark in algebraic complexity and a basic subroutine in linear algebra, graph algorithms, and symbolic computation.
The Coppersmith--Winograd tensor and the laser method produced the strongest bounds on $\omega$ for several decades. The 1990 tensor-square analysis gave $\omega<2.375477$. Later analyses of larger powers improved the numerical bound, but they organized their recursion through values assigned independently to constituent tensors. Duan, Wu, and Zhou identified a loss in that organization: several fine constituents that can coexist inside one coarse block may be counted as though they had to be selected independently. Their asymmetric-hashing framework partially compensates for this **combination loss**. The paper's full second-power specialization improves the best bound obtainable from the square of the Coppersmith--Winograd tensor to $\omega<2.374631$; see Section 6.3 and its parameter Table 2 in [Duan--Wu--Zhou](https://arxiv.org/abs/2210.10173).
This mission isolates that second-power result. It is smaller than the paper's record-setting eighth-power calculation, but it contains the genuinely new asymmetric-hashing and hole-repair mechanisms in their first complete form. It therefore provides a focused bridge from the existing formalization of the classical $2.375477$ square analysis to later combination-loss methods.
## Setting
For a field $K$, the **matrix-multiplication tensor**
$$
\langle a,b,c\rangle_K
=\sum_{i<a}\sum_{j<b}\sum_{k<c}
x_{ij}\otimes y_{jk}\otimes z_{ki}
$$
encodes multiplication of an $a\times b$ matrix by a $b\times c$ matrix. A restriction applies one linear map to each tensor leg, while a degeneration permits polynomial families of such maps and takes their first nonzero coefficient. A degeneration from the diagonal tensor $I_r$ gives a border-rank upper bound of $r$.
The **Coppersmith--Winograd tensor** with parameter $q$ is
$$
CW_q=
\sum_{i=1}^{q}
(x_i y_i z_0+x_i y_0z_i+x_0y_i z_i)
+x_0y_0z_{q+1}+x_0y_{q+1}z_0+x_{q+1}y_0z_0.
$$
It has border rank at most $q+2$. Its coordinate partition has six supported types, and the square $CW_q^{\otimes2}$ has fifteen coarse constituent types $(i,j,k)$ with $i+j+k=4$. A large tensor power contains many blocks with prescribed joint and marginal type distributions. The **laser method** retains blocks whose variables are disjoint and interprets their direct sum through Schönhage's asymptotic sum inequality.
Duan--Wu--Zhou refine this organization by also retaining a split distribution for the fine indices inside each coarse constituent. Coarse $X$- and $Y$-blocks are made unique, while compatible coarse triples may initially share a $Z$-block. The resulting partially damaged constituent tensors are described as broken copies of a standard-form tensor. The formal target uses $q=6$, the full Section 6 construction, and the paper's released second-power parameters.
## Formalization targets
### Goal: the full second-power asymmetric-hashing bound
For every field $K$,
$$
\operatorname{matMulExp}(K)<\frac{23747}{10000}=2.3747.
$$
The source reports the stronger numerical endpoint $2.374631$, so the displayed rational inequality has strict slack. The Lean declaration has exactly the same field quantification and uses exactly the same `matMulExp` definition as the existing Coppersmith--Winograd $2.376$ mission; only the theorem name and rational endpoint change.
### Source-level milestones
The mission first isolates the available-block shuffling interface extracted from Definitions 5.3--5.5 and Claims 5.8--5.10, then formalizes the finite covering core of the Hole Lemma 5.6. The subsequent tensor realization by zeroing and identification, the multiple-copy Corollary 5.11, the compatibility-rate identity of Lemma 6.7, the probabilistic part of Claim 6.8, and the global restricted-splitting value inequality in Equation (25) remain visible structural leaves rather than being hidden inside scalar assumptions. The numerical milestone instantiates Equation (25) with the exact $q=6$ data of Section 6.3 and Table 2 and checks a strict value surplus at $\tau=23747/30000$. The structural proof must also make explicit the conversion from the paper's six-symmetrized value to a direct `HasTauValueAtLeast` witness for the mode-symmetric CW square. The final bridge applies the existing tau-value/rank machinery and transfers the Strassen-preorder exponent bound to `matMulExp`.
## Significance
The mathematical result gives the first improvement over the classical Coppersmith--Winograd number while continuing to use only the tensor square. It separates improvement of the tensor analysis from improvement obtained merely by moving to a much higher tensor power. The same standard-form and restricted-splitting language is then reused by the paper's higher-power algorithm, which reports $\omega<2.371866$.
For formalization, the mission adds reusable infrastructure for nested tensor partitions. Existing CW-square work records coarse support types and actual matrix-multiplication restrictions. This mission extends that layer with fine split distributions, compatibility between levels, broken-block bookkeeping, and repair of holes without replacing tensor statements by unverified scalar values. Those definitions are prerequisites for later asymmetric-hashing, complete-split, and more-asymmetry analyses.
The bound is known mathematically and was published at FOCS 2023. The open work is a machine-checked reconstruction. The underlying CW tensor, border-rank certificate, canonical tensor-square grading, Salem--Spencer sets, direct-sum tau-value notion, asymptotic sum inequality, and exponent equivalence already exist on Prove2Me. The new frontier is the cross-level combination-loss analysis and its exact numerical specialization.
## Difficulty
The central difficulty is that coarse and fine decompositions cannot be optimized independently. Two coarse triples may share a $Z$-block, and a fine $Z$-block can be useful for one triple, compatible with several, or removed by a collision. Counting all locally valuable fine constituents therefore does not certify a direct sum. Conversely, requiring every coarse $Z$-block to be unique discards precisely the combinations that produce the improvement.
The Hole Lemma must also preserve the actual tensor. A broken copy lacks some fine variable blocks; combining several such copies is useful only when a degeneration covers every required block with controlled loss and does not duplicate monomials. On the numerical side, the same-marginal maximum-entropy term and restricted-splitting values must be bounded with certified real inequalities. Floating-point output from MATLAB is evidence for a witness, not a Lean proof.
## Formalization scope
The mission uses the existing `TensorObj`, `MMObj`, restriction, degeneration, asymptotic-rank, `HasTauValueAtLeast`, `matMulExp_strassen`, and `matMulExp` declarations in environment `777aaa61dcd2a1258d2b4962dbe983ede4d23b2e`. Top-level results quantify over an arbitrary field. Finite supports and block indices are represented by finite types; probability and split distributions are nonnegative real functions of total mass one; entropy and numerical optimization live in the reals.
The formalization is restricted to $CW_6^{\otimes2}$ for the capstone, although generic definitions and source lemmas may quantify over levels and finite index types. A valid proof must connect scalar rate inequalities to witnessed restrictions or degenerations yielding direct sums of concrete matrix-multiplication tensors. A constant-valued surrogate for the restricted-splitting value, a hypothesis that already assumes the desired exponent bound, or a certificate definition containing its own conclusion is outside scope.
Contributions are welcome for standard-form tensor encodings, finite permutation arguments, hole repair, type and split counting, entropy maximization certificates, certified logarithm and power inequalities, and the final tau-value/rank assembly. Statements should identify the corresponding definition, lemma, claim, equation, or table in the source.
## Selected references
- Ran Duan, Hongxun Wu, and Renfei Zhou, *Faster Matrix Multiplication via Asymmetric Hashing*, 64th IEEE Symposium on Foundations of Computer Science (FOCS), 2023. [arXiv:2210.10173](https://arxiv.org/abs/2210.10173) and [released verification code](https://osf.io/dta6p/).
- Don Cop persmith and Shmuel Winograd, *Matrix Multiplication via Arithmetic Progressions*, Journal of Symbolic Computation 9, 1990, pp. 251--280. [DOI 10.1016/S0747-7171(08)80013-2](https://doi.org/10.1016/S0747-7171(08)80013-2).
- Arnold Schönhage, *Partial and Total Matrix Multiplication*, SIAM Journal on Computing 10(3), 1981, pp. 434--455. [DOI 10.1137/0210032](https://doi.org/10.1137/0210032).
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Captain: wenxinzhang
Primal-Dual Online Load Balancing on Unrelated MachinesTextbook
## The model
Fix $m \ge 1$ **machines** and $n$ **jobs** arriving one at a time in the order $0, \dots, n-1$. Job $i$ carries a whole vector of nonnegative **loads** $\tilde p(i,j)$, one per machine, with no assumed relationship between the entries — the same job may be cheap on one machine and unplaceable on another. This is the **unrelated machines** model. When job $i$ arrives its load vector becomes visible, and the algorithm must commit it to a single machine immediately and **irrevocably**, knowing nothing about the jobs still to come. A machine's load is the sum of $\tilde p(i,j)$ over the jobs assigned to it.
The setting formalized here is one **normalized phase**: loads are already scaled by a guessed makespan, so machine $j$ counts as **eligible** for job $i$ exactly when $\tilde p(i,j) \le 1$. The phase is allowed to give up rather than assign badly — it **fails** if an arriving job has no eligible machine, or if an internal weight grows past $1$.
## The algorithm and the guarantee
The algorithm keeps a **weight** $x(j)$ per machine, initialized to $1/(2m)$. Job $i$ goes to the eligible machine $\ell$ minimizing $\tilde p(i,\ell)\, x(\ell)$; that machine's weight is then scaled by $1 + \tilde p(i,\ell)/2$, so a machine becomes exponentially unattractive as it fills. The weights are the primal variables of the covering LP
$$\min \sum_j x(j) + \sum_i z(i) \quad \text{s.t.} \quad \tilde p(i,j)\,x(j) + z(i) \ge 1 \ \text{ for every eligible pair } (i,j),$$
and each assignment raises one dual variable $y(i,\ell)$ to $1$. The guarantee follows from weak duality rather than a bespoke potential argument, which is the point of the primal-dual method.
The goal theorem states that if the dual admits a feasible solution putting unit total mass on every job — the certificate that the guessed makespan was large enough — then the phase does not fail, every job is assigned, and every machine ends with load
$$\sum_{i \,\text{assigned to}\, j} \tilde p(i,j) \ \le\ \frac{\ln(3m)}{\ln(3/2)}.$$
The source states this as $O(\log m)$; the explicit constant is what its proof yields.
Note that the load bound alone is not the theorem: it holds vacuously when the phase assigns nothing, and the milestones state it that way deliberately. The content is the conjunction of *succeeded*, *assigns all*, and the bound.
## Scope
The **doubling wrapper** — guess a makespan, run a phase, double the guess and restart on failure — is what turns this phase into an $O(\log m)$-competitive online algorithm. It is outside this mission; the guarantee proved here is the conditional single-phase statement. The milestones break the argument into weak duality for finite LPs, the load bound, primal feasibility at each prefix, the primal objective identity, and the failure certificate.
## Source
Niv Buchbinder and Joseph (Seffi) Naor, *The Design of Competitive Online Algorithms via a Primal-Dual Approach*, Foundations and Trends in Theoretical Computer Science 3(2–3), 2009, Chapter 8, pp. 193–196 (Theorem 8.1). [PDF](https://www.tau.ac.il/~nivb/download/pd-survey.pdf) · [doi:10.1561/0400000024](https://doi.org/10.1561/0400000024)
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Captain: marwahaha
Sensitivity ConjectureResearch Paper
Nearly every measure of Boolean function complexity was known to be equivalent — except sensitivity. Proving the conjecture unified the whole picture.
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Captain: marwahaha
Davie–Stothers Fourth-Power Bound: omega < 2.3737Research Paper
## Motivation
The **matrix-multiplication exponent** $\omega$ measures the asymptotic arithmetic cost of multiplying square matrices. An upper bound $\omega<c$ means that, over the field under consideration, two $n\times n$ matrices can be multiplied using $O(n^{c+\varepsilon})$ field operations for every $\varepsilon>0$. It is a central benchmark in algebraic complexity and controls the exponent of many algorithms that use matrix multiplication as a subroutine.
Coppersmith and Winograd's 1990 analysis of the square of their tensor established $\omega<2.375477$. That number remained the record for roughly two decades. Stothers' 2010 thesis first obtained a smaller exponent by analyzing the fourth tensor power, and Davie and Stothers later supplied a self-contained journal treatment. Their Theorem 5.3 and numerical parameters give $\omega<2.373689703$; see [Davie--Stothers](https://www.maths.ed.ac.uk/~sandy/a11164.pdf), printed pp. 367--368. The result is the first historical step below the classical tensor-square barrier and is the natural next capstone after a formal proof of the $2.375477$ bound.
This mission formalizes the Davie--Stothers fourth-power argument at the exact rational endpoint $2.3737$. It concentrates on the new mathematical layer introduced by the fourth power: five non-matrix constituents, their recursive value estimates, and the two-dimensional same-marginal ambiguity in the final distribution count.
## Setting
For a field $K$, an order-three tensor represents a bilinear map. The **matrix-multiplication tensor**
$$
\langle a,b,c\rangle_K
=\sum_{i<a}\sum_{j<b}\sum_{k<c}
x_{ij}\otimes y_{jk}\otimes z_{ki}
$$
encodes multiplication of an $a\times b$ matrix by a $b\times c$ matrix. Restrictions apply linear maps to the three tensor legs; degenerations permit polynomial families of maps. A direct sum of matrix-multiplication tensors has disjoint variable blocks and can be converted into an exponent inequality by Schönhage's asymptotic sum inequality.
The **Coppersmith--Winograd tensor** $CW_q$ has border rank at most $q+2$ and a three-class coordinate partition. Its square decomposes into fifteen coarse constituents $\varphi_{ijk}$ with $i+j+k=4$. Davie--Stothers square this decomposition again. The fourth power has forty-five constituents with indices summing to eight, grouped into ten symmetry classes represented by
$$
\varphi_{008},\ \varphi_{017},\ \varphi_{026},\ \varphi_{035},\ \varphi_{044},
\ \varphi_{116},\ \varphi_{125},\ \varphi_{134},\ \varphi_{224},\ \varphi_{233}.
$$
The first five classes are rectangular matrix-multiplication tensors. The last five require recursive value bounds. With $\rho\in[2,3]$, the paper writes
$$
E=(2q)^\rho,\qquad H=(q^2+2)^\rho,\qquad
L=4q^\rho(q^\rho+2),
$$
and states the five lower bounds in Lemma 5.1. The final fourth-power extraction assigns frequencies to the ten symmetry classes. Their coordinate marginals are encoded by the $9\times10$ matrix $Q$ in Equation (5.2); its kernel is the two-dimensional space $Y$ displayed immediately after that equation.
The paper's bounds are limiting exponential rates and may carry subexponential losses in their finite Salem--Spencer extractions. Prove2Me's `HasTauValueAtLeast` predicate instead records a constant-relative finite witness. The source-faithful formal statements therefore assert attainment of every fixed nonnegative base strictly below each displayed limiting rate, rather than unjustified attainment of the limiting endpoint itself. This downward-closed form retains the complete asymptotic conclusion and is exactly what the final strict numerical surplus needs.
## Formalization targets
### Goal: the Davie--Stothers fourth-power bound
For every field $K$,
$$
\operatorname{matMulExp}(K)<\frac{23737}{10000}=2.3737.
$$
The source's computed endpoint $2.373689703$ is strictly smaller, giving slack for an exact rational certificate. The Lean goal has exactly the same field quantification and `matMulExp` definition as the existing Coppersmith--Winograd mission; only the theorem identifier and endpoint change.
### Source-level milestones
The mission records the canonical nine-grading of $CW_6^{\otimes4}$ and the ten symmetry classes of Table 1. It formalizes all five clauses of Lemma 5.1 for $\varphi_{116}$, $\varphi_{125}$, $\varphi_{134}$, $\varphi_{224}$, and $\varphi_{233}$ in every-strict-lower-base form; Equation (5.2) and the stated basis of $\ker Q$; Lemma 5.2's entropy minimization along that kernel; Theorem 5.3's downward-closed fourth-power value inequality; and the Table 2 numerical specialization. The final milestones connect the resulting tau-value surplus to the border-rank budget and transfer the Strassen-preorder exponent bound to `matMulExp`.
## Significance
Mathematically, this theorem is the first improvement obtained by passing from the square to the fourth power of the Coppersmith--Winograd tensor. It establishes the recursive constituent pattern used by the later eighth-, sixteenth-, and higher-power analyses. In particular, the five formulas in Lemma 5.1 are the first complete catalogue of genuinely recursive fourth-power constituents.
For formalization, the mission creates a reusable representation of higher-power CW gradings and their symmetry orbits. It also forces a distinction between a locally chosen joint type and all other types with the same marginals. Lemma 5.2 is the exact finite-dimensional entropy correction needed when the marginal map has nontrivial kernel. That infrastructure can be reused by later refined-laser and complete-split missions.
The result is known mathematically. The open task is a machine-checked reconstruction. Prove2Me already contains the CW tensor, its characteristic-free border-rank degeneration, its canonical square grading and constituent restrictions, the Salem--Spencer layer, direct-sum tau-value witnesses, the asymptotic sum inequality, and the exponent bridge. The exact optimizer identity for the $\varphi_{116}$ profile is also proved. The remaining frontier is to connect the literal fourth-power constituents to finite direct-sum extractions, then assemble all five value estimates and the final kernel-corrected distribution count.
## Difficulty
The fourth power contains 225 ordered products before symmetry grouping. A formal proof must show that each claimed constituent is the literal block of $CW_q^{\otimes4}$ and that its recursive decomposition uses the correct variable spaces. Replacing a sum of overlapping blocks by an external direct sum would make the value bound artificially strong.
The five non-matrix classes have different feasible frequency polytopes. Their optimizer formulas are valid only after the corresponding nonnegativity and normalization conditions are checked. The $\varphi_{233}$ class already has a nontrivial same-marginal family. At the global level the map $Q$ has a two-dimensional kernel, so marginal counts alone do not determine a unique joint distribution. Ignoring that kernel removes the entropy penalty and invalidates Theorem 5.3.
Finally, Table 2 contains decimal witnesses obtained numerically. A formal proof must replace floating-point evaluation by exact rational parameters and certified bounds for logarithms and real powers, while retaining strict slack at $23737/10000$.
## Formalization scope
The development uses environment `777aaa61dcd2a1258d2b4962dbe983ede4d23b2e` and the existing `TensorObj`, `MMObj`, restriction, degeneration, `HasTauValueAtLeast`, `tensorAsymptoticRank`, `matMulExp_strassen`, and `matMulExp` declarations. Top-level theorems quantify over an arbitrary field. Finite block indices and symmetry classes use finite types; frequency vectors and entropy inequalities use real numbers; exact finite profiles use natural numbers before passing to cofinal asymptotics.
The capstone specializes to $q=6$ and the fourth tensor power. Generic grading, orbit, multinomial, entropy, and optimizer lemmas are welcome when they shorten later missions. Every value theorem must ultimately be backed by restrictions or degenerations to direct sums of concrete matrix-multiplication tensors. An opaque value functional, a constituent definition that is an external sum rather than the source block, or a numerical hypothesis that assumes the desired endpoint is outside scope.
Contributions are welcome for the literal nine-grading, symmetry-orbit classification, the five constituent extractions, exact address factorizations, optimizer feasibility, the kernel calculation and Lemma 5.2, exact Table 2 arithmetic, and the final exponent assembly.
## Selected references
- A. M. Davie and A. J. Stothers, *Improved Bound for Complexity of Matrix Multiplication*, Proceedings of the Royal Society of Edinburgh Section A: Mathematics 143(2), 2013, pp. 351--369. [Author PDF](https://www.maths.ed.ac.uk/~sandy/a11164.pdf) and [DOI 10.1017/S0308210511001646](https://doi.org/10.1017/S0308210511001646).
- A. J. Stothers, *On the Complexity of Matrix Multiplication*, PhD thesis, University of Edinburgh, 2010. [Edinburgh Research Archive](https://era.ed.ac.uk/handle/1842/4734).
- Don Coppersmith and Shmuel Winograd, *Matrix Multiplication via Arithmetic Progressions*, Journal of Symbolic Computation 9, 1990, pp. 251--280. [DOI 10.1016/S0747-7171(08)80013-2](https://doi.org/10.1016/S0747-7171(08)80013-2).
- Arnold Schönhage, *Partial and Total Matrix Multiplication*, SIAM Journal on Computing 10(3), 1981, pp. 434--455. [DOI 10.1137/0210032](https://doi.org/10.1137/0210032).
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Captain: Shuze Chen
Algorithmic Game Theory I: Existence of Nash EquilibriumTextbook
## Motivation
The strategic-form game is the basic object of noncooperative game theory, and the **Nash equilibrium** — a profile of randomized strategies from which no player benefits by deviating unilaterally — is its central solution concept. Nash proved in 1951 that every game with finitely many players and finite strategy sets has such an equilibrium ([Nash, *Non-cooperative games*, Ann. Math. 54 (1951)](https://doi.org/10.2307/1969529)); this single existence theorem is the reason the concept organizes the rest of the field, from the computational complexity of finding equilibria to the price of anarchy. The theorem is stated as Theorem 1.8 of Nisan–Roughgarden–Tardos–Vazirani (eds.), *Algorithmic Game Theory* (Cambridge, 2007), the source text of this mission series, whose first chapter (Tardos–Vazirani) also treats the two special cases that admit direct algorithmic proofs: two-person zero-sum games, where equilibria are exactly the optimal solutions of a dual pair of linear programs (von Neumann 1928; Theorem 1.11), and a simple linear market, where equilibrium prices are computed by an ascending tight-set algorithm (Theorem 1.17).
A timeline of the existence theorem: von Neumann (1928) proved the minimax theorem for two-person zero-sum games; Nash (1950, 1951) extended existence to arbitrary finite games, first via Kakutani's fixed-point theorem and then via Brouwer's. All known proofs of the general theorem pass through a fixed-point principle, and this is not an artifact: computing a Nash equilibrium is PPAD-complete (Daskalakis–Goldberg–Papadimitriou 2009; Chen–Deng–Teng 2009), and PPAD is precisely the complexity class of the fixed-point arguments.
## Setting
A **finite strategic-form game** consists of a finite set $\iota$ of players, for each player $i$ a finite nonempty set $S_i$ of **pure strategies**, and for each player a **payoff function** $u_i : \prod_j S_j \to \mathbb{R}$; all players are utility maximizers. A **mixed strategy** for player $i$ is a probability distribution on $S_i$, represented as a weight function $\sigma_i : S_i \to \mathbb{R}$ with $\sigma_i \ge 0$ and $\sum_{s} \sigma_i(s) = 1$ (a **lottery**). Players randomize independently, so a **mixed profile** $\sigma = (\sigma_i)_{i}$ induces the product distribution on pure strategy vectors, and player $i$'s **expected payoff** is
$$U_i(\sigma) \;=\; \sum_{s \in \prod_j S_j} \Big(\prod_j \sigma_j(s_j)\Big)\, u_i(s).$$
A mixed profile $\sigma$ is a **(mixed) Nash equilibrium** if for every player $i$ and every lottery $\tau$ on $S_i$, replacing $\sigma_i$ by $\tau$ does not increase $U_i$.
A **two-person zero-sum game** is given by a matrix $A \in \mathbb{R}^{m \times n}$: the row player picks a row distribution $p$, the column player a column distribution $q$, and the column player pays the row player $p^{\mathsf T} A q$ in expectation.
The market of §1.8.1 of the source has finitely many divisible **goods**, good $a$ in $s_a$ units, and finitely many **buyers**, buyer $j$ bringing budget $m_j > 0$ and interested in a nonempty set of goods; utilities are linear 0/1, so a buyer wants any goods from her interest set and none other. **Market-clearing prices** are positive prices under which each buyer can spend her whole budget on cheapest goods in her interest set while every good sells out exactly.
## Formalization targets
### Goal (capstone) — Theorem 1.8
$$\text{Every finite strategic-form game has a mixed Nash equilibrium.}$$
Stated for an arbitrary finite family of finite nonempty strategy types; no bound on the number of players, no genericity assumptions.
### Supporting — Brouwer fixed-point theorem
$$K \subseteq E \text{ nonempty compact convex},\ E \text{ finite-dimensional},\ f : K \to K \text{ continuous} \implies \exists x,\ f(x) = x.$$
Mathlib currently has no form of Brouwer's theorem; every known proof of Theorem 1.8 needs it (or an equivalent), so it enters the mission as an explicit milestone rather than an assumed library fact.
### Theorem 1.11 — zero-sum games
$$\exists\, p^\ast, q^\ast:\quad \forall p,\ p^{\mathsf T} A q^\ast \le {p^\ast}^{\mathsf T} A q^\ast, \quad \forall q,\ {p^\ast}^{\mathsf T} A q^\ast \le {p^\ast}^{\mathsf T} A q, \quad\text{and}\quad (p^\ast, q^\ast) \text{ is a mixed Nash equilibrium}$$
of the explicit two-player game with payoffs $A_{xy}$ to the row player and $-A_{xy}$ to the column player. The source states the result as: optimal solutions of a dual pair of LPs form a Nash equilibrium of the zero-sum game; the first two conjuncts are the saddle point that LP optimality amounts to, and the third states the Nash-equilibrium clause against the mission's own game vocabulary, so "zero-sum" is formal (the two payoffs sum to zero) rather than implicit in the shape of the statement.
### Theorem 1.17 (existence form)
$$\text{The 0/1-utilities linear market admits market-clearing prices and allocations.}$$
The source proves this by an ascending-price algorithm and also bounds its running time; the complexity half has no formal counterpart in this mission.
## Significance
The capstone is the foundation of the whole mission series: correlated equilibria, price-of-anarchy bounds, and mechanism-design characterizations in later missions all quantify over or compare against Nash equilibria, and the series inherits its game vocabulary (`IsLottery`, `IsMixedProfile`, `expectedPayoff`, `IsMixedNash`) from this mission.
Formalizing it produces the first Brouwer fixed-point theorem in this environment — a well-known gap in mathlib with reuse value far beyond game theory (every degree-theoretic and equilibrium-existence argument needs it). The zero-sum milestone yields the minimax theorem, reusable for the learning-dynamics mission that follows. All results here are classical and proved on paper; the work requested is machine-checked proof, not new mathematics.
## Difficulty
The central difficulty is Brouwer. The standard routes are (i) Sperner's lemma plus a limit argument, which needs a formal theory of simplicial subdivisions that does not exist in mathlib; (ii) algebraic topology (no retraction of the ball onto the sphere), for which mathlib has singular homology but not yet the homology of spheres in usable form; (iii) analytic proofs (Milnor–Rogers). None is short; the milestone is deliberately stated for a general nonempty compact convex set in a finite-dimensional normed space so that any route serves, and so the lemma lands in reusable generality.
Given Brouwer, Theorem 1.8 still requires Nash's gain-function construction on the product of simplices and the verification that fixed points are equilibria — bookkeeping-heavy but standard. Theorem 1.11 does not need Brouwer: mathlib's Sion minimax theorem (`Mathlib.Topology.Sion`) applies to the bilinear payoff on the product of standard simplices, or one can argue by LP duality directly. Theorem 1.17 needs the tight-set/max-flow argument of Lemmas 1.15–1.16 or any direct construction of the equilibrium.
## Formalization scope
Games are presented concretely: players form a finite index type, strategies a finite type per player, payoffs are functions into $\mathbb{R}$; mixed strategies are weight functions with a `IsLottery` predicate, not measure-theoretic distributions. Deviations in the equilibrium definition range over all lotteries (not only pure strategies): the pure-deviation reduction is a lemma a solver may prove, not part of the definition. Strategy sets are assumed nonempty in the capstone; the player set need not be. In the zero-sum milestone both dimensions are positive (`Fin (m+1)`, `Fin (n+1)`), payoffs flow from the column player to the row player, `stdSimplex` plays the role of the mixed-strategy space, and the Nash-equilibrium conjunct is stated for the Boolean-indexed two-player game built by `matrixGameStrat`/`zeroSumPayoff`/`matrixGameProfile` from the definitions bundle. In the market milestone all supplies and budgets are positive, every buyer's interest set is nonempty, and every good has an interested buyer, matching the standing assumptions of §1.8.1; allocations are recorded as money spent, so the clearing condition is $\sum_j x_{ja} = p_a s_a$ with no division anywhere.
Trivializing readings are ruled out: the empty simplex has no lotteries, so nonemptiness hypotheses appear exactly where their absence would make an existence claim false (Brouwer on the empty set, games with an empty strategy set, zero-dimensional matrix games).
## Selected references
- J. F. Nash, *Non-cooperative games*, Annals of Mathematics 54 (1951), 286–295. [DOI](https://doi.org/10.2307/1969529)
- J. von Neumann, *Zur Theorie der Gesellschaftsspiele*, Mathematische Annalen 100 (1928), 295–320. [DOI](https://doi.org/10.1007/BF01448847)
- N. Nisan, T. Roughgarden, É. Tardos, V. V. Vazirani (eds.), *Algorithmic Game Theory*, Cambridge University Press, 2007, Chapter 1. [DOI](https://doi.org/10.1017/CBO9780511800481)
- C. Daskalakis, P. W. Goldberg, C. H. Papadimitriou, *The complexity of computing a Nash equilibrium*, SIAM J. Computing 39 (2009), 195–259. [DOI](https://doi.org/10.1137/070699652)
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Captain: Shuze Chen
The k-Server ConjectureOpen Problem
## Motivation
The $k$-server problem was introduced by Manasse, McGeoch, and Sleator ([STOC 1988 / J. Algorithms 1990](https://doi.org/10.1016/0196-6774(90)90003-W)) as a common generalization of paging, weighted caching, and related sequential decision problems, and their **$k$-server conjecture** has since become the central open question of competitive analysis. The conjecture asserts that a single ratio — exactly $k$ — governs deterministic online server management on *every* metric space.
## Timeline
- **1985.** Sleator and Tarjan introduce competitive analysis — an online algorithm judged against the offline optimum on every input — for list update and paging, and ask for a theory of such guarantees.
- **1988–1990.** Manasse, McGeoch, and Sleator introduce the $k$-server problem ([STOC 1988; J. Algorithms 1990](https://doi.org/10.1016/0196-6774(90)90003-W)) and settle its extremes: no deterministic algorithm beats ratio $k$ on any space with more than $k$ points (Corollary 7), two servers admit a $2$-competitive algorithm (Theorem 5, algorithm RES), and $k$ servers on $k+1$ points admit a $k$-competitive one (Theorem 4, algorithm BAL). Section 8 poses the **$k$-server conjecture**, in the symmetric finite setting of the paper.
- **1990.** Fiat, Rabani, and Ravid ([FOCS 1990](https://doi.org/10.1109/FSCS.1990.89566)) give the first competitive ratio depending on $k$ alone — exponential in $k$, but finite on every metric space.
- **1991.** Chrobak, Karloff, Payne, and Vishwanathan ([SIAM J. Discrete Math.](https://doi.org/10.1137/0404017)) prove the conjecture on the real line via **Double Coverage**; Chrobak and Larmore ([SIAM J. Comput.](https://doi.org/10.1137/0220008)) extend it to all tree metrics.
- **1995.** Koutsoupias and Papadimitriou ([J. ACM](https://doi.org/10.1145/210118.210128)) prove the **Work Function Algorithm** is $(2k-1)$-competitive on every metric space — the breakthrough, and still the best general bound. Their Conjecture 1.1 fixes the conjecture's modern form: *for every metric space there is an online algorithm with competitive ratio $k$.*
- **1996.** The same authors verify the conjecture on spaces of $k+2$ points via the dual **2-evader problem** (Inf. Process. Lett. 57).
- **2004.** Bartal and Koutsoupias prove the WFA itself is $k$-competitive on the line, weighted stars, and all spaces of $k+2$ points.
- **2021.** Coester and Koutsoupias ([ICALP](https://arxiv.org/abs/2102.10474)) give a unifying potential for all known WFA analyses and push the frontier to the circle.
- **2023.** Bubeck, Coester, and Rabani ([STOC](https://arxiv.org/abs/2211.05753)) refute the *randomized* analogue: no $o(\log^2 k)$-competitive randomized algorithm exists in general. The deterministic conjecture — this mission's goal — survives as the central open question, with the gap between $k$ and $2k-1$ unmoved since 1995.
## Setting
Fix a metric space $M$ with distance function $d$, and a number of servers $k \ge 1$. A **configuration** records where the $k$ servers stand: it is a function $C$ assigning to each server $i \in \{1, \dots, k\}$ a point $C(i) \in M$. Moving the servers from configuration $C$ to configuration $C'$ means server $i$ travels from $C(i)$ to $C'(i)$; the **movement cost** is the total distance traveled,
$$\mathrm{moveCost}(C, C') \;=\; \sum_{i=1}^{k} d\bigl(C(i),\, C'(i)\bigr).$$
A **request sequence** is a finite list $\sigma = (r_1, \dots, r_n)$ of points of $M$, presented one at a time; write $\sigma_{\le j} = (r_1, \dots, r_j)$ for the list of the first $j$ requests (so $\sigma_{\le 0}$ is the empty list).
A **deterministic online algorithm** $A$ is a rule that, for every finite request sequence $\ell$, specifies a configuration $A(\ell)$ — where the servers stand after serving the requests of $\ell$ in order. In particular $A(\text{empty list})$ is the **initial configuration**, before any request arrives. Two points about this way of modeling an algorithm:
- *Online and deterministic, by construction.* The configuration after $j$ requests is $A(\sigma_{\le j})$, a function of those first $j$ requests only — the algorithm cannot see the future, and makes no random choices.
- *The service constraint.* Whenever a request sequence ends with a request $r$, some server must stand at $r$ immediately after: for every list $\ell$ and every point $r$, the configuration reached after serving $\ell$ followed by $r$ places at least one server at the point $r$.
Running $A$ on $\sigma = (r_1, \dots, r_n)$ produces the configurations $A(\sigma_{\le 0}),\, A(\sigma_{\le 1}),\, \dots,\, A(\sigma_{\le n})$, and its **cost** is the total movement along this trajectory:
$$\mathrm{cost}_A(\sigma) \;=\; \sum_{j=1}^{n} \mathrm{moveCost}\bigl(A(\sigma_{\le j-1}),\, A(\sigma_{\le j})\bigr).$$
For comparison, an **offline schedule** for $\sigma$ starting at a configuration $C_0$ is any sequence of configurations $S_0 = C_0, S_1, \dots, S_n$ in which $S_j$ places a server at the request $r_j$, for each $j$ — chosen with the whole of $\sigma$ known in advance. The **optimal offline cost** $\mathrm{OPT}(C_0, \sigma)$ is the infimum, over all such schedules, of the total movement $\sum_{j=1}^{n} \mathrm{moveCost}(S_{j-1}, S_j)$.
Finally, $A$ is **$c$-competitive** if there is a constant $a$ — depending on the algorithm, hence possibly on the metric space and the initial configuration, but never on the request sequence — with
$$\mathrm{cost}_A(\sigma) \;\le\; c \cdot \mathrm{OPT}\bigl(A(\text{empty list}),\, \sigma\bigr) + a \qquad \text{for every request sequence } \sigma.$$
## Formalization targets
### Goal — the $k$-server conjecture
$$\text{For every } k \ge 1,\ \text{every metric space } M,\ \text{and every initial configuration } C_0:\ \exists\, A \text{ starting at } C_0 \text{ that is } k\text{-competitive.}$$
The goal fixes no algorithm: any $k$-competitive construction settles it. This is the weakest stable form of the conjecture — it survives every improvement in constants or techniques short of a disproof.
### Milestones — the known ladder
The milestones are the classical results between the trivial and the conjectured, each an existence or impossibility statement over the same definitions: the lower bound $c \ge k$ on any space with at least $k+1$ points; the conjecture for $k = 2$; for spaces of exactly $k+1$ points; for the real line; the $(2k-1)$ upper bound of the Work Function Algorithm on every space; the conjecture for spaces of exactly $k+2$ points; the conjecture for three servers in the Manhattan plane $(\mathbb{R}^2, \ell^1)$ — the one settled case over a genuinely two-dimensional continuum (Bein–Chrobak–Larmore 2002; reproved by the unifying potential of Coester–Koutsoupias 2021); Coester–Koutsoupias's 2021 result that the Work Function Algorithm *itself* — not just some algorithm — is $3$-competitive for three servers on trees, stated over an explicit formalization of the WFA; and the 2023 Bubeck–Coester–Rabani refutation of the *randomized* analogue: there are $(k+1)$-point spaces on which every randomized algorithm is $\Omega(\log^2 k)$-competitive, stated over a mixed-strategy model of randomized online algorithms.
## Significance
A proof of the conjecture would close the founding problem of competitive analysis and pin down the exact power of determinism in online optimization over arbitrary metrics; a disproof would separate general metric spaces from every special class where the ratio $k$ is known tight. Either outcome recalibrates the field's standard model of adversarial request sequences.
None of these results — not even the lower bound — has a machine-checked proof, and online algorithms as a subject are absent from Mathlib. This mission builds the base layer: a faithful model of online service systems (configurations, online algorithms as prefix functions, offline schedules, competitiveness), the classical possibility and impossibility results over it, and, at the top, the Koutsoupias–Papadimitriou bound, whose potential-function argument is self-contained but delicate. The model is reusable for paging, weighted caching, metrical task systems, and the randomized $k$-server problem.
## Difficulty
The obvious first idea — the greedy algorithm, moving the nearest server to each request — is not competitive for any constant, already on three points of the line: two nearby points can ping-pong one server forever while a server parked slightly farther away never moves. Every known competitive algorithm must sometimes move a server *other than* the nearest one, and the whole difficulty of the conjecture is quantifying exactly how much such foresight-free hedging can achieve. The Work Function Algorithm's analysis via a potential over offline work functions loses a factor of two for reasons nobody has been able to remove; on the lower-bound side, no metric space is known where the deterministic ratio exceeds $k$.
## Formalization scope
The Lean model commits to: configurations as functions `Fin k → M` (**labeled** servers — equivalent in cost to the unlabeled multiset model, since offline can permute labels for free); algorithms as total functions `List M → (Fin k → M)` with the service constraint, so a step may move several servers (the standard laziness reduction makes this equivalent to one-move-per-request); costs in `ℝ` via `Metric.dist`; the offline optimum as an `sInf` over schedules, which agrees with the attained minimum on finite spaces; and the additive-constant form of competitiveness, quantified as `∃ a, ∀ σ`.
Two conventions guard against trivialization. The additive constant is quantified *before* the request sequence — allowing it to depend on $\sigma$ would make every algorithm $1$-competitive. And the lower-bound milestone requires $k+1$ *distinct* points (`Finset.card = k + 1`); on spaces with at most $k$ points the conjecture is trivially true and the lower bound false.
Three further definitional layers extend the model. The **work function** `workFunction C₀ σ C` is the `sInf` of (schedule cost + final move to `C`) over schedules serving `σ` from `C₀`, and the **Work Function Algorithm** `WFA` is defined on finite spaces with `k ≥ 1` servers: after each request it moves to a configuration containing the request minimizing (movement cost) + (work function of the history including the request), a minimizer existing by finiteness and ties broken by a fixed arbitrary choice — matching the standard definition with its "ties broken arbitrarily" (our fixed choice is one admissible instance). A **tree** is a finite metric space carrying a tree graph whose weighted path lengths realize the metric — exactly "the set of vertices of a tree" of the sources. A **randomized algorithm** is a mixed strategy: a probability measure over an index type together with a deterministic algorithm per outcome and measurable per-sequence cost; its expected cost is a lower Lebesgue integral in $[0,\infty]$, and $c$-competitiveness from $C_0$ demands every outcome start at $C_0$ and one additive constant work for all request sequences.
Welcome contributions: proofs of any milestone in any order (the lower bound and the $(k+1)$-point case are the natural entry points); alternative algorithms for milestones already closed; and infrastructure lemmas about `moveCost`, schedules, and work functions published as reusable platform theorems.
## Selected references
- M. Manasse, L. McGeoch, D. Sleator, *Competitive algorithms for server problems*, J. Algorithms 11 (1990). [doi:10.1016/0196-6774(90)90003-W](https://doi.org/10.1016/0196-6774(90)90003-W)
- A. Fiat, Y. Rabani, Y. Ravid, *Competitive k-server algorithms*, FOCS 1990. [doi:10.1109/FSCS.1990.89566](https://doi.org/10.1109/FSCS.1990.89566)
- M. Chrobak, H. Karloff, T. Payne, S. Vishwanathan, *New results on server problems*, SIAM J. Discrete Math. 4 (1991). [doi:10.1137/0404017](https://doi.org/10.1137/0404017)
- M. Chrobak, L. Larmore, *An optimal on-line algorithm for k servers on trees*, SIAM J. Comput. 20 (1991). [doi:10.1137/0220008](https://doi.org/10.1137/0220008)
- E. Koutsoupias, C. Papadimitriou, *On the k-server conjecture*, J. ACM 42 (1995). [doi:10.1145/210118.210128](https://doi.org/10.1145/210118.210128)
- E. Koutsoupias, C. Papadimitriou, *The 2-evader problem*, Inf. Process. Lett. 57(5) (1996), 249–252.
- C. Coester, E. Koutsoupias, *Towards the k-server conjecture: a unifying potential, pushing the frontier to the circle*, ICALP 2021. [arXiv:2102.10474](https://arxiv.org/abs/2102.10474)
- S. Bubeck, C. Coester, Y. Rabani, *The randomized k-server conjecture is false!*, STOC 2023. [arXiv:2211.05753](https://arxiv.org/abs/2211.05753)
- E. Koutsoupias, *The k-server problem* (survey), Computer Science Review 3 (2009). [doi:10.1016/j.cosrev.2009.04.002](https://doi.org/10.1016/j.cosrev.2009.04.002)
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Captain: Shuze Chen
The 4/3 Conjecture for Metric TSPOpen Problem
## Motivation
The traveling salesman problem — visit $n$ cities by the cheapest round trip — is the most widely known problem in combinatorial optimization, and its central open question concerns a linear program. The **subtour-elimination relaxation** (the Held–Karp bound) replaces tours by fractional edge weights, and both in theory and in practice (it powers the lower bounds inside the Concorde solver) it is remarkably close to the true optimum. How close, in the worst case, is the **integrality gap** of the relaxation: the supremum of $\mathrm{OPT}/\mathrm{LP}$ over metric instances. Explicit instance families push the gap up to $4/3$; the best proven upper bound sits just barely below $3/2$. The **4/3 conjecture** — the gap is exactly $4/3$ — has been the benchmark question of approximation algorithms for four decades.
## Timeline
- **1954.** Dantzig, Fulkerson, and Johnson solve a 49-city instance by hand with the cutting planes that become the subtour-elimination LP.
- **1970–1971.** Held and Karp introduce the 1-tree/Lagrangian bound and show it equals the subtour LP value — since then, "the Held–Karp bound".
- **1976/1978.** Christofides, and independently Serdyukov, give the $3/2$-approximation: minimum spanning tree plus a matching on odd-degree vertices.
- **1980.** Wolsey ([Math. Prog. Study 13](https://doi.org/10.1007/BFb0120913)) shows Christofides' analysis goes through against the LP: $\mathrm{OPT} \le \frac{3}{2}\,\mathrm{LP}$, so the integrality gap is at most $3/2$. Shmoys and Williamson ([IPL 1990](https://doi.org/10.1016/0020-0190(90)90028-V)) rediscover this via a monotonicity property.
- **1995.** Goemans ([Math. Programming 69](https://doi.org/10.1007/BF01585563)) analyzes the worst-case ratios of TSP relaxations and states the $4/3$ conjecture explicitly; the $4/3$ lower-bound families (three parallel paths) are by then folklore.
- **2011–2014.** For **graph metrics** (shortest-path metrics of unweighted graphs) the barrier breaks: Oveis Gharan–Saberi–Singh and Mömke–Svensson beat $3/2$, and Sebő–Vygen ([Combinatorica 2014](https://arxiv.org/abs/1201.1870)) reach $7/5$ — the conjectured-optimal shape of progress, but only for a special class.
- **2020–2022.** Karlin, Klein, and Oveis Gharan prove a $3/2 - \varepsilon$ approximation for general metric TSP ([STOC 2021](https://arxiv.org/abs/2007.01409)) and then an integrality-gap bound $\gamma \le 3/2 - \varepsilon$ with $\varepsilon > 10^{-36}$ ([FOCS 2022](https://arxiv.org/abs/2105.10043)), via max-entropy sampling of spanning trees and strongly Rayleigh distributions — the first general improvement over Wolsey in forty years, by an astronomically small margin.
- **Today.** The gap between the $4/3$ lower bound and the $3/2 - 10^{-36}$ upper bound is the conjecture. For half-integral LP solutions — where the conjectured extremal instances live — the bound has been pushed to $1.4983$ (Gupta, Lee, Li, Mucha, Newman, and Sarkar, via matroid-based rounding).
## Setting
An **instance** on $n \ge 3$ cities is a cost function $c$ assigning to each ordered pair of cities $u, v$ a real cost $c(u,v)$, required to be a **metric cost**: symmetric ($c(u,v) = c(v,u)$), zero on the diagonal ($c(v,v) = 0$), and satisfying the triangle inequality $c(u,w) \le c(u,v) + c(v,w)$. Nonnegativity follows; distinct cities at distance zero are allowed, as usual for metric TSP.
A **tour** visits every city exactly once and returns to its start. Formally a tour is given by an ordering: a permutation $\pi$ of the cities, traversed as $\pi(0), \pi(1), \dots, \pi(n-1)$ and back to $\pi(0)$; its cost $\mathrm{tourCost}(c, \pi)$ is the sum of the costs of consecutive steps, and $\mathrm{OPT}(c)$ — written `tspOpt c` — is the minimum over all orderings.
The **subtour-elimination (Held–Karp) relaxation** replaces the tour by a fractional edge weight $x(u,v)$ for each pair of cities. A weight vector $x$ is **feasible** (`IsHeldKarp x`) when it is symmetric with zero diagonal, has entries in $[0,1]$, gives every city fractional degree two ($\sum_u x(v,u) = 2$), and crosses every nontrivial cut at least twice: for every set $S$ of cities other than $\emptyset$ and all cities, $\sum_{u \in S} \sum_{v \notin S} x(u,v) \ge 2$. The **Held–Karp bound** `hkValue c` is the infimum of $\frac{1}{2}\sum_u \sum_v c(u,v)\,x(u,v)$ over feasible $x$ (the double sum counts each edge twice, hence the $\frac12$). The incidence vector of any tour is feasible, so $\mathrm{LP} \le \mathrm{OPT}$ always.
## Formalization targets
### Goal — the 4/3 conjecture
$$\mathrm{OPT}(c) \;\le\; \tfrac{4}{3}\,\mathrm{LP}(c) \qquad \text{for every } n \ge 3 \text{ and every metric cost } c.$$
Together with the known lower-bound families this says the integrality gap is exactly $4/3$. The goal carries no algorithm and no constant to improve: it is the terminal statement of the ladder, open in both directions (a proof or a counterexample instance would each settle it).
### Milestones — the known ladder
Five results over the same definitions: the relaxation is valid ($\mathrm{LP} \le \mathrm{OPT}$); instance families force the gap arbitrarily close to $4/3$; tree doubling gives $\mathrm{OPT} \le 2\,\mathrm{LP}$; Wolsey's theorem gives $\mathrm{OPT} \le \frac{3}{2}\,\mathrm{LP}$, the classical upper bound; and the Karlin–Klein–Oveis Gharan record $\mathrm{OPT} \le (\frac{3}{2} - \varepsilon)\,\mathrm{LP}$ for some $\varepsilon > 10^{-36}$ (FOCS 2022). The last milestone is a statement-level target: its known proof (max-entropy sampling, strongly Rayleigh polynomials) is far beyond current formalization practice, so the mission's usable proving frontier remains Wolsey — the milestone records the state of the art as a formal statement.
## Significance
The 4/3 conjecture is the reference open problem of approximation algorithms: the quality of the subtour LP calibrates every algorithmic advance on TSP, and the conjectured extremal instances guide the search for better rounding schemes. The bound is also what practical solvers actually compute — branch-and-cut on this LP solves instances with tens of thousands of cities — so the conjecture is a statement about the observed tightness of the world's most-used combinatorial lower bound.
Nothing in this circle exists in any proof assistant: Mathlib has no TSP, no LP relaxations, no polyhedral combinatorics of tours. The mission's milestones force the base layer into existence — tours over `Equiv.Perm`, cut constraints over `Finset`, and, for the upper bounds, the parity and tree arguments (spanning trees against the LP, T-joins for the $3/2$ bound) whose infrastructure is reusable for matching theory and network design far beyond TSP.
## Difficulty
The naive plan — round the LP solution to a tour — has no known analysis losing less than $3/2$ in general, and the half-integral extremal instances show the hard cases are structured and simple-looking at once. Christofides' matching argument is provably stuck at $3/2$ against the LP; forty years of work moved the constant by $10^{-36}$, and that advance needed an entirely new probabilistic toolkit. On the other side, no instance family with ratio above $4/3$ has ever been found despite extensive computational search over small instances (Benoit–Boyd and successors). Both directions of the goal are genuinely open territory.
## Formalization scope
The Lean model commits to: cities `Fin n`; costs `c : Fin n → Fin n → ℝ` with `IsMetricCost` (symmetry, zero diagonal, triangle inequality — nonnegativity is derived, and semimetrics are included as in the standard statement of the conjecture); tours as orderings `π : Equiv.Perm (Fin n)` traversed cyclically via `finRotate`, so every permutation denotes a Hamiltonian cycle and every Hamiltonian cycle is denoted; both optimal values as `sInf` over nonempty, bounded-below sets of reals, so they are genuine minima for `n ≥ 3`. The hypothesis `3 ≤ n` is load-bearing: for `n ≤ 2` the degree-2 constraints are infeasible, `sInf ∅ = 0` by convention, and the bounds would be false — every theorem therefore carries it.
Welcome contributions: the milestones in any order — `held_karp_le_opt` is the natural entry point (the tour's incidence vector crosses every cut at least twice); `integrality_gap_lower_bound` needs the three-path instance family and a case analysis on its tours; `tree_doubling_bound` needs spanning trees against the LP; `wolsey_bound` adds the T-join/parity argument and is the summit. Reusable infrastructure — spanning tree polytopes, T-joins, Eulerian traversals, cut lemmas — is welcome as platform theorems. Graph-TSP ($7/5$), path TSP, and asymmetric TSP are deliberately left to future missions; the Karlin–Klein–Oveis Gharan bound is stated as a milestone, but its sampling machinery is expected to arrive, if ever, as shared infrastructure built over many contributions.
## Selected references
- G. Dantzig, R. Fulkerson, S. Johnson, *Solution of a large-scale traveling-salesman problem*, Oper. Res. 2 (1954).
- M. Held, R. Karp, *The traveling-salesman problem and minimum spanning trees*, Oper. Res. 18 (1970); Part II, Math. Programming 1 (1971).
- N. Christofides, *Worst-case analysis of a new heuristic for the travelling salesman problem*, CMU report (1976); A. Serdyukov, Upravlyaemye Sistemy 17 (1978).
- L. Wolsey, *Heuristic analysis, linear programming and branch and bound*, Math. Prog. Study 13 (1980). [doi:10.1007/BFb0120913](https://doi.org/10.1007/BFb0120913)
- D. Shmoys, D. Williamson, *Analyzing the Held-Karp TSP bound: a monotonicity property with application*, Inf. Process. Lett. 35 (1990). [doi:10.1016/0020-0190(90)90028-V](https://doi.org/10.1016/0020-0190(90)90028-V)
- M. Goemans, *Worst-case comparison of valid inequalities for the TSP*, Math. Programming 69 (1995). [doi:10.1007/BF01585563](https://doi.org/10.1007/BF01585563)
- A. Sebő, J. Vygen, *Shorter tours by nicer ears*, Combinatorica 34 (2014). [arXiv:1201.1870](https://arxiv.org/abs/1201.1870)
- A. Karlin, N. Klein, S. Oveis Gharan, *A (slightly) improved approximation algorithm for metric TSP*, STOC 2021. [arXiv:2007.01409](https://arxiv.org/abs/2007.01409)
- A. Karlin, N. Klein, S. Oveis Gharan, *A (slightly) improved bound on the integrality gap of the subtour LP for TSP*, FOCS 2022. [arXiv:2105.10043](https://arxiv.org/abs/2105.10043)
- V. Traub, J. Vygen, *Approximation Algorithms for Traveling Salesman Problems*, Cambridge University Press, 2024. [book page](https://www.or.uni-bonn.de/tspbook/book.pdf)
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