Descent algorithm for nonsmooth stochastic multiobjective optimization

DOI10.1007/S10589-017-9921-XzbMath1387.90237OpenAlexW2727285023MaRDI QIDQ1687314

Jean-Antoine Désidéri, Fabrice Poirion, Quentin Mercier

Publication date: 22 December 2017

Published in: Computational Optimization and Applications (Search for Journal in Brave)

Full work available at URL: https://hal.inria.fr/hal-01660788/file/articlePQD-COAP.pdf

zbMATH Keywords

multiobjective optimizationalmost sure convergencestochasticnonsmooth

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Stochastic programming (90C15)

Related Items (4)

Non-convex multiobjective optimization under uncertainty: a descent algorithm. Application to sandwich plate design and reliability ⋮ Adaptive sampling stochastic multigradient algorithm for stochastic multiobjective optimization ⋮ FlexiBO: A Decoupled Cost-Aware Multi-Objective Optimization Approach for Deep Neural Networks ⋮ Efficient Multi-objective Reinforcement Learning via Multiple-gradient Descent with Iteratively Discovered Weight-Vector Sets

Uses Software

• GradSamp

Cites Work

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• Multiple-gradient descent algorithm (MGDA) for multiobjective optimization
• Stochastic optimization over a Pareto set associated with a stochastic multi-objective optimization problem
• Stochastic multiobjective optimization: Sample average approximation and applications
• Nonlinear multiobjective optimization
• Methods of descent for nondifferentiable optimization
• A subgradient method for multiobjective optimization
• Introduction to Nonsmooth Optimization
• Multiple subgradient descent bundle method for convex nonsmooth multiobjective optimization
• Survey of Bundle Methods for Nonsmooth Optimization
• A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization
• Approximating Subdifferentials by Random Sampling of Gradients

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