A Randomised Non-descent Method for Global Optimisation

Authors

Dmitry A. Pasechnyuk, Moscow Institute of Physics and Technology & Mohamed bin Zayed University of Artificial IntelligenceFollow
Alexander Gornov, Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences

Document Type

Conference Proceeding

Publication Title

Communications in Computer and Information Science

Abstract

This paper proposes novel algorithm for non-convex multimodal constrained optimisation problems. It is based on sequential solving restrictions of problem to sections of feasible set by random subspaces (in general, manifolds) of low dimensionality. This approach varies in a way to draw subspaces, dimensionality of subspaces, and method to solve restricted problems. We provide empirical study of algorithm on convex, unimodal and multimodal optimisation problems and compare it with efficient algorithms intended for each class of problems.

First Page

3

Last Page

14

DOI

10.1007/978-3-031-48751-4_1

Publication Date

12-14-2023

Keywords

Random subspace, Subspace sampling, Zeroth-order optimization

Comments

Preprint version from arXiv

Uploaded on June 20, 2024

Recommended Citation

D. Pasechnyuk and A. Gornov, "A Randomised Non-descent Method for Global Optimisation," Communications in Computer and Information Science, vol. 1913 CCIS, pp. 3 - 14, Dec 2023. doi: 10.1007/978-3-031-48751-4_1