An accelerated continuous greedy algorithm for maximizing strong submodular functions

Zengfu Wang, Bill Moran, Xuezhi Wang, Quan Pan

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

An accelerated continuous greedy algorithm is proposed for maximization of a special class of non-decreasing submodular functions f:2X→R+ subject to a matroid constraint with a (Formula Presented.)(1-e-c-ε) approximation for any ε > 0, where c is the curvature with respect to the optimum. Functions in the special class of submodular functions satisfy the criterion ∀A,B⊆X,∀j∈X\(A∪B), ▵fj(A,B)=Δf(A∪{j})+f(B∪{j})-f((A∩B)∪{j})-f(A∪B∪{j})-[f(A)+f(B)-f(A∩B)-f(A∪B)]≤0. As an alternative to the standard continuous greedy algorithm, the proposed algorithm can substantially reduce the computational expense by removing redundant computational steps and, therefore, is able to efficiently handle the maximization problems for this special class of submodular functions. Examples of such functions are presented.

Original languageEnglish
Pages (from-to)1107-1124
Number of pages18
JournalJournal of Combinatorial Optimization
Volume30
Issue number4
DOIs
StatePublished - 1 Nov 2015

Keywords

  • Approximation algorithm
  • Matroid
  • Monotone submodular set function

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