English

A note on the parameter $\ell$ in Buchbinder--Feldman's deterministic submodular matroid algorithm

Data Structures and Algorithms 2026-05-01 v1

Abstract

Buchbinder and Feldman recently gave a deterministic (11/eε)(1-1/e-\varepsilon)-approximation for maximizing a non-negative monotone submodular function subject to a matroid constraint, with query complexity O~ε(nr)\widetilde{O}_\varepsilon(nr). Their algorithm uses an integer parameter \ell, which Buchbinder and Feldman fix to =1+1/ε\ell = 1 + \lceil 1/\varepsilon \rceil via a loose bound on (1+1/)(1+1/\ell)^{-\ell}. We point out two purely elementary refinements. First, the classical P\'olya--Szeg\H{o} inequality (1+1/)e1(1+1/(2))(1+1/\ell)^{-\ell} \le e^{-1}(1+1/(2\ell)) replaces the loose step in their proof and permits =1/(2eε)\ell = \lceil 1/(2e\varepsilon) \rceil, shrinking the hidden constant in O~ε(nr)\widetilde{O}_\varepsilon(nr) by a factor 20.816/ε\approx 2^{0.816/\varepsilon}. Second, an alternating-series tail bound for log(1+t)\log(1+t) yields the asymptotically sharp inequality (1+1/)e1exp(1/(2)1/(32)+1/(43))(1+1/\ell)^{-\ell} \le e^{-1}\exp(1/(2\ell) - 1/(3\ell^2) + 1/(4\ell^3)), matching the true expansion of (1+1/)(1+1/\ell)^{-\ell} through order 3\ell^{-3} and translating into =1/(2eε)5/12+O(ε)\ell_\star = 1/(2e\varepsilon) - 5/12 + O(\varepsilon). The asymptotic class O~ε(nr)\widetilde{O}_\varepsilon(nr) of the query complexity is unchanged in either case; only the implicit constant in ε\varepsilon is improved. All inequalities in this note are formalized and machine-checked in Lean 4 against Mathlib.

Cite

@article{arxiv.2604.27362,
  title  = {A note on the parameter $\ell$ in Buchbinder--Feldman's deterministic submodular matroid algorithm},
  author = {Shisheng Li},
  journal= {arXiv preprint arXiv:2604.27362},
  year   = {2026}
}

Comments

8 pages. Companion Lean 4 formalization at https://github.com/daizisheng/bf24-note

R2 v1 2026-07-01T12:42:48.453Z