Work

Random Descent Steps in a Probability Maximization Scheme

Year: 2025

Type: article

Abstract: Gradient computation of multivariate distribution functions calls for considerable effort. Hence coordinate descent and derivative-free approaches are attractive. This paper deals with constrained convex problems. We perform random descent steps in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We prove that the scheme converges and present a computational study comparing different descent methods applied in the approximation scheme.

Source: Journal of Optimization Theory and Applications

DOI: 10.1007/s10957-025-02619-2

Authors: Edit Csizmás, Rajmund Drenyovszki, Tamás Szántai, Csaba I. Fábián

Institutions: John von Neumann University, Budapest University of Technology and Economics

Language: English

Topic: Advanced Optimization Algorithms Research

Subfield: Numerical Analysis

Field: Mathematics

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,190

Funders: Neumann János Egyetem, Magyarország Kormánya, European Regional Development Fund, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap