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Main Authors: Oki, Taihei, Shioura, Akiyoshi
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2503.01110
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author Oki, Taihei
Shioura, Akiyoshi
author_facet Oki, Taihei
Shioura, Akiyoshi
contents We consider the minimization of an M-convex function, which is a discrete convexity concept for functions on the integer lattice points. It is known that a minimizer of an Mconvex function can be obtained by the steepest descent algorithm. In this paper, we propose an effective use of long step length in the steepest descent algorithm, aiming at the reduction in the running time. In particular, we obtain an improved time bound by using long step length. We also consider the constrained M-convex function minimization and show that long step length can be applied to a variant of steepest descent algorithm as well.
format Preprint
id arxiv_https___arxiv_org_abs_2503_01110
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Steepest Descent Algorithm for M-convex Function Minimization Using Long Step Length
Oki, Taihei
Shioura, Akiyoshi
Optimization and Control
We consider the minimization of an M-convex function, which is a discrete convexity concept for functions on the integer lattice points. It is known that a minimizer of an Mconvex function can be obtained by the steepest descent algorithm. In this paper, we propose an effective use of long step length in the steepest descent algorithm, aiming at the reduction in the running time. In particular, we obtain an improved time bound by using long step length. We also consider the constrained M-convex function minimization and show that long step length can be applied to a variant of steepest descent algorithm as well.
title Steepest Descent Algorithm for M-convex Function Minimization Using Long Step Length
topic Optimization and Control
url https://arxiv.org/abs/2503.01110