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Proof of Theorem 3, steps (ii)–(iii), corrected — k≤2ε OPT(I)+1k \le 2\varepsilon\,OPT(I) + 1k≤2εOPT(I)+1

Proved
KKBinPacking.LinearGrouping.alg1k_le

by mikedeng1 · Sep 28, 2026 · Mathlib 0df444a (Lean v4.33.1)

approximation-algorithmsbin-packingp2o-batch-p200bp2o-gran-per-chapterp2o-plan-paperp2o-v1

Let ε>0\varepsilon > 0ε>0 and let III be an instance. Throughout, JJJ is the instance of Step 1 of ALGORITHM 1 (the pieces larger than max⁡(1/n(I),ε/2)\max(1/n(I), \varepsilon/2)max(1/n(I),ε/2)), k=⌈n(J)ε2⌉k = \lceil n(J)\varepsilon^2\rceilk=⌈n(J)ε2⌉, and KKK, K′K'K′ are the outputs of linear grouping of JJJ with parameter kkk. Then

k=⌈n(J)ε2⌉≤2ε OPT(I)+1.k = \lceil n(J)\varepsilon^2\rceil \le 2\varepsilon\,OPT(I) + 1 .k=⌈n(J)ε2⌉≤2εOPT(I)+1.

This bounds the number of bins used in Step 3 of ALGORITHM 1 for the pieces of K′K'K′.

Formalization Note The paper prints (ii) OPT(J)≥SIZE(J)≥ε n(J)OPT(J) \ge SIZE(J) \ge \varepsilon\, n(J)OPT(J)≥SIZE(J)≥εn(J) and (iii) k≤ε OPT(J)+1≤ε OPT(I)+1k \le \varepsilon\,OPT(J) + 1 \le \varepsilon\,OPT(I) + 1k≤εOPT(J)+1≤εOPT(I)+1. Step (ii) is false: the pieces of JJJ exceed ε/2\varepsilon/2ε/2, not ε\varepsilonε, so only SIZE(J)≥(ε/2) n(J)SIZE(J) \ge (\varepsilon/2)\,n(J)SIZE(J)≥(ε/2)n(J) holds. For ε=1/10\varepsilon = 1/10ε=1/10 and III consisting of 19 00019\,00019000 pieces of size 0.0510.0510.051 one has J=IJ = IJ=I, k=190k = 190k=190, OPT(I)=1000OPT(I) = 1000OPT(I)=1000, and SIZE(J)=969<ε n(J)=1900SIZE(J) = 969 < \varepsilon\, n(J) = 1900SIZE(J)=969<εn(J)=1900, while ε OPT(I)+1=101<190\varepsilon\,OPT(I) + 1 = 101 < 190εOPT(I)+1=101<190. The statement here is the bound the proof yields once (ii) is corrected.

Preamble
import Mathlib
import Definitions.Def_KKBinPacking_LinearGrouping_Instance
import Definitions.Def_KKBinPacking_Shared_ConfigLP
import Definitions.Def_KKBinPacking_Shared_AnyFit
import Definitions.Def_KKBinPacking_LinearGrouping_LinGroup
import Definitions.Def_KKBinPacking_LinearGrouping_Algorithm1
open KKBinPacking.Shared
Formal statement
namespace KKBinPacking.LinearGrouping
theorem alg1k_le (ε : ℝ) (hε : 0 < ε) (I : Multiset ℝ) (hI : IsInstance I) :
    (alg1k ε I : ℝ) ≤ 2 * ε * (OPT I : ℝ) + 1 := by sorry
end KKBinPacking.LinearGrouping
Source
Karmarkar, Karp, An Efficient Approximation Scheme for the One-Dimensional Bin-Packing Problem, Proc. 23rd FOCS, 1982, p. 316, Theorem 3, proof steps (ii)–(iii) (left column), corrected
Human review
  • Endorsed by Shuze Chen · Oct 1, 2026

    Confirmed by the moderator at approval.

  • Endorsed by mikedeng1 · Oct 1, 2026

    Confirmed by the mission captain (proposal self-audit).

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