A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00383481" target="_blank" >RIV/68407700:21230/25:00383481 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1137/23M1569460" target="_blank" >https://doi.org/10.1137/23M1569460</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1137/23M1569460" target="_blank" >10.1137/23M1569460</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems

Popis výsledku v původním jazyce

A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algorithm is unique from other interior-point methods for solving smooth nonconvex optimization problems since the search directions are computed using stochastic gradient estimates. It is also unique in its use of inner neighborhoods of the feasible region—defined by a positive and vanishing neighborhood-parameter sequence—in which the iterates are forced to remain. It is shown that with a careful balance between the barrier, step size, and neighborhood sequences, the proposed algorithm satisfies convergence guarantees in both deterministic and stochastic settings. The results of numerical experiments show that in both settings the algorithm can outperform projection-based methods.

Název v anglickém jazyce

A Stochastic-Gradient-Based Interior-Point Algorithm for Solving Smooth Bound-Constrained Optimization Problems

Popis výsledku anglicky

A stochastic-gradient-based interior-point algorithm for minimizing a continuously differentiable objective function (that may be nonconvex) subject to bound constraints is presented, analyzed, and demonstrated through experimental results. The algorithm is unique from other interior-point methods for solving smooth nonconvex optimization problems since the search directions are computed using stochastic gradient estimates. It is also unique in its use of inner neighborhoods of the feasible region—defined by a positive and vanishing neighborhood-parameter sequence—in which the iterates are forced to remain. It is shown that with a careful balance between the barrier, step size, and neighborhood sequences, the proposed algorithm satisfies convergence guarantees in both deterministic and stochastic settings. The results of numerical experiments show that in both settings the algorithm can outperform projection-based methods.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Výzkumné centrum informatiky</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

SIAM Journal on Optimization

ISSN

1052-6234

e-ISSN

1095-7189

Svazek periodika

35

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

30

Strana od-do

1030-1059

Kód UT WoS článku

001504739900013

EID výsledku v databázi Scopus

2-s2.0-105005263643