Random Descent Steps in a Probability Maximization Scheme
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.
Authors
- Csaba I. Fábián (ORCID: https://orcid.org/0000-0002-9446-1566)
- Edit Csizmás (ORCID: https://orcid.org/0000-0003-4397-1758)
- Rajmund Drenyovszki (ORCID: https://orcid.org/0000-0002-9462-2729)
- Tamás Szántai
Institutions
- Budapest University of Technology and Economics (HU)
- John von Neumann University (HU)
Publication Details
- Journal
- Journal of Optimization Theory and Applications
- Published
- 2025-02-18
- DOI
- https://doi.org/10.1007/s10957-025-02619-2
- Primary Topic
- Stochastic Gradient Optimization Techniques
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Neumann János Egyetem
- Magyarország Kormánya
- European Regional Development Fund
- Nemzeti Kutatási, Fejlesztési és Innovaciós Alap