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Regrouping a chunk system, preserving trivial initial information

Proved
KServer.chunk_regroup_stable_init

by Gabewhigham · Sep 7, 2026 · Mathlib c5ea003 (Lean v4.30.0)

k-serveronline-algorithmsprobability

Let CCC be a chunk system with online escapes on a metric space XXX with marked points s,ts,ts,t: a finitely supported random sequence of mmm chunks of set requests, equipped with a filtration mathcalF0subseteqmathcalF1subseteqcdots\\mathcal F_0\\subseteq\\mathcal F_1\\subseteq\\cdotsmathcalF0​subseteqmathcalF1​subseteqcdots, adapted sizes c0,dots,cm−1in[0,cmax]c_0,\\dots,c_{m-1}\\in[0,c_{\\max}]c0​,dots,cm−1​in[0,cmax​] of expected total at least TTT, and the conditional cost bound: for every chunk index iii, every evader EEE and every online escape rule with escape price ppp,

ci;le;mathbbEbig[,mathrmbailcostE(textchunki,,p);big∣;mathcalFibig].c_i \\;\\le\\; \\mathbb E\\big[\\,\\mathrm{bailcost}_E(\\text{chunk } i,\\,p)\\;\\big|\\;\\mathcal F_i\\big].ci​;le;mathbbEbig[,mathrmbailcostE​(textchunki,,p);big∣;mathcalFi​big].

Chunks produced by the recursive construction of the randomized Omega(log2k)\\Omega(\\log^2 k)Omega(log2k) lower bound come in a wide range of sizes. The regrouping step of Bubeck-Coester-Rabani's Lemma 15 merges consecutive chunks into super-chunks of an almost prescribed size Theta\\ThetaTheta, which is what lets the induction be applied on the next level.

Theorem. Suppose the sizes of CCC lie in [0,cmax][0,c_{\\max}][0,cmax​], the initial information is trivial, the expected total satisfies MThetaleTM\\Theta\\le TMThetaleT for some Mge1M\\ge 1Mge1 and some Thetagecmax\\Theta\\ge c_{\\max}Thetagecmax​, and the conditional expected remaining size Fh=mathbbE[sumjgehcjmidmathcalFh]F_h=\\mathbb E[\\sum_{j\\ge h}c_j\\mid\\mathcal F_h]Fh​=mathbbE[sumjgeh​cj​midmathcalFh​] satisfies both

Fh−cmaxleFh+1quadtext(slowdecrement),qquadtextstylesumalej<bcjleFa−Fb+cmaxquadtext(boundedsurprise)F_h - c_{\\max}\\le F_{h+1}\\quad\\text{(slow decrement)},\\qquad \\textstyle\\sum_{a\\le j<b}c_j\\le F_a-F_b+c_{\\max}\\quad\\text{(bounded surprise)}Fh​−cmax​leFh+1​quadtext(slowdecrement),qquadtextstylesumalej<b​cj​leFa​−Fb​+cmax​quadtext(boundedsurprise)

pointwise. Then for every p′gep+Theta+3cmaxp'\\ge p+\\Theta+3c_{\\max}p′gep+Theta+3cmax​ there exists a chunk system with trivial initial information on the same space, serving the same request sequences, with exactly MMM chunks, all of whose sizes lie in the narrow window [Theta−cmax,Theta+cmax][\\Theta-c_{\\max},\\Theta+c_{\\max}][Theta−cmax​,Theta+cmax​], of expected total MThetaM\\ThetaMTheta and escape price p′p'p′.

The super-chunks are cut at the stopping times hi=minh:FhleF0−iThetah_i=\\min\\{h: F_h\\le F_0-i\\Theta\\}hi​=minh:Fh​leF0​−iTheta, and the size assigned to the iii-th super-chunk is mathbbE[Fhi−Fhi+1midmathcalFhi]\\mathbb E[F_{h_i}-F_{h_{i+1}}\\mid\\mathcal F_{h_i}]mathbbE[Fhi​​−Fhi+1​​midmathcalFhi​​]. Slow decrement is what keeps this quantity from undershooting Theta\\ThetaTheta by more than cmaxc_{\\max}cmax​. The cost bound for a super-chunk is obtained by charging the original chunks one by one up to the first moment the escape rule fires inside the super-chunk; bounded surprise is what bounds the mass of the chunks after that moment by Theta+3cmax\\Theta+3c_{\\max}Theta+3cmax​, which is exactly the extra escape price that has been reserved.

Formalization note. Both hypotheses are branchwise strengthenings of statements that hold automatically in conditional expectation, and they are stated here explicitly because a branchwise stopping-time argument consumes them branchwise. The conclusion is phrased as non-emptiness of the type of output chunk systems.

Preamble
import Mathlib
import Definitions.Def_KServer_chunk_system_b
import Definitions.Def_KServer_chunk_bounded_surprise
Formal statement
namespace KServer

theorem chunk_regroup_stable_init
    {X : Type*} [MetricSpace X] {s t : X} {cHi total price : ℝ}
    (C : ChunkSystemB X s t 0 cHi total price 0) (Θ : ℝ) (M : ℕ)
    (hcHi : 0 ≤ cHi) (hΘ : cHi ≤ Θ) (hM0 : 0 < M)
    (h0 : ∀ ω₁ ω₂ : C.Ω, C.hist 0 ω₁ = C.hist 0 ω₂)
    (hslow : C.SlowDecrement cHi)
    (hsurp : C.BoundedSurprise cHi)
    (hMtot : (M : ℝ) * Θ ≤ total)
    (price' : ℝ) (hprice' : price + (Θ + 3 * cHi) ≤ price') :
    ∃ D : ChunkSystemB X s t (Θ - cHi) (Θ + cHi) ((M : ℝ) * Θ) price' M,
      ∀ ω₁ ω₂ : D.Ω, D.hist 0 ω₁ = D.hist 0 ω₂ := by sorry

end KServer
Source
Bubeck, Coester, Rabani, Shortest paths without a map, but with an entropic regularizer, arXiv:2211.05753v2, Section 5, Lemma 15

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