Scaling algorithms for M-convex function minimization

Satoko Moriguchi, Kazuo Murota, Akiyoshi Shioura

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

M-convex functions have various desirable properties as convexity in discrete optimization. We can find a global minimum of an M-convex function by a greedy algorithm, i.e., so-called descent algorithms work for the minimization. In this paper, we apply a scaling technique to a greedy algorithm and propose an efficient algorithm for the minimization of an M-convex function. Computational results are also reported.

Original languageEnglish
Pages (from-to)922-929
Number of pages8
JournalIEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
VolumeE85-A
Issue number5
Publication statusPublished - 2002 May

Keywords

  • Convex function
  • Discrete optimization
  • Matroid
  • Scaling algorithm

ASJC Scopus subject areas

  • Signal Processing
  • Computer Graphics and Computer-Aided Design
  • Electrical and Electronic Engineering
  • Applied Mathematics

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