On a minimization problem of the maximum generalized eigenvalue: properties and algorithms

Fuente: arXiv
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Main Authors: Nishioka, Akatsuki, Toyoda, Mitsuru, Tanaka, Mirai, Kanno, Yoshihiro
Format: Preprint
Published: 2023
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author Nishioka, Akatsuki
Toyoda, Mitsuru
Tanaka, Mirai
Kanno, Yoshihiro
author_facet Nishioka, Akatsuki
Toyoda, Mitsuru
Tanaka, Mirai
Kanno, Yoshihiro
contents We study properties and algorithms of a minimization problem of the maximum generalized eigenvalue of symmetric-matrix-valued affine functions, which is nonsmooth and quasiconvex, and has application to eigenfrequency optimization of truss structures. We derive an explicit formula of the Clarke subdifferential of the maximum generalized eigenvalue and prove the maximum generalized eigenvalue is a pseudoconvex function, which is a subclass of a quasiconvex function, under suitable assumptions. Then, we consider smoothing methods to solve the problem. We introduce a smooth approximation of the maximum generalized eigenvalue and prove the convergence rate of the smoothing projected gradient method to a global optimal solution in the considered problem. Also, some heuristic techniques to reduce the computational costs, acceleration and inexact smoothing, are proposed and evaluated by numerical experiments.
format Preprint
id arxiv_https___arxiv_org_abs_2312_01603
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle On a minimization problem of the maximum generalized eigenvalue: properties and algorithms
Nishioka, Akatsuki
Toyoda, Mitsuru
Tanaka, Mirai
Kanno, Yoshihiro
Optimization and Control
90C26, 90C90
We study properties and algorithms of a minimization problem of the maximum generalized eigenvalue of symmetric-matrix-valued affine functions, which is nonsmooth and quasiconvex, and has application to eigenfrequency optimization of truss structures. We derive an explicit formula of the Clarke subdifferential of the maximum generalized eigenvalue and prove the maximum generalized eigenvalue is a pseudoconvex function, which is a subclass of a quasiconvex function, under suitable assumptions. Then, we consider smoothing methods to solve the problem. We introduce a smooth approximation of the maximum generalized eigenvalue and prove the convergence rate of the smoothing projected gradient method to a global optimal solution in the considered problem. Also, some heuristic techniques to reduce the computational costs, acceleration and inexact smoothing, are proposed and evaluated by numerical experiments.
title On a minimization problem of the maximum generalized eigenvalue: properties and algorithms
topic Optimization and Control
90C26, 90C90
url https://arxiv.org/abs/2312.01603