Descent algorithm for nonsmooth stochastic multiobjective optimization

An algorithm for solving the expectation formulation of stochastic nonsmooth multiobjective optimization problems is proposed. The proposed method is an extension of the classical stochastic gradient algorithm to multiobjective optimization using the properties of a common descent vector defined in...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Computational optimization and applications Ročník 68; číslo 2; s. 317 - 331
Hlavní autori: Poirion, Fabrice, Mercier, Quentin, Désidéri, Jean-Antoine
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.11.2017
Springer Nature B.V
Springer Verlag
Predmet:
Algorithms
Analysis of PDEs
Convex and Discrete Geometry
Descent
Fluid mechanics
Management Science
Mathematical programming
Mathematics
Mathematics and Statistics
Mechanics
Multiple objective analysis
Operations Research
Operations Research/Decision Theory
Optimization
Pareto optimum
Physics
Statistics
Stochastic models
Nonsmooth
Almost sure convergence
Stochastic
Multiobjective optimization
15
Multiobjective optimization · Stochastic · Nonsmooth · Almost
sure convergence
ISSN:0926-6003, 1573-2894
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:An algorithm for solving the expectation formulation of stochastic nonsmooth multiobjective optimization problems is proposed. The proposed method is an extension of the classical stochastic gradient algorithm to multiobjective optimization using the properties of a common descent vector defined in the deterministic context. The mean square and the almost sure convergence of the algorithm are proven. The algorithm efficiency is illustrated and assessed on an academic example.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0926-6003
1573-2894
DOI:10.1007/s10589-017-9921-x