Improved front steepest descent for multi-objective optimization

DOI10.1016/j.orl.2023.03.001zbMath1525.90388arXiv2301.03310OpenAlexW4323349933MaRDI QIDQ6106527

Matteo Lapucci, Pierluigi Mansueto

Publication date: 3 July 2023

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2301.03310

zbMATH Keywords

multi-objective optimizationsteepest descentPareto front

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Multi-objective and goal programming (90C29) Methods of reduced gradient type (90C52)

Cites Work

• On the convergence of steepest descent methods for multiobjective optimization
• Steepest descent methods for multicriteria optimization.
• Scalarizing vector optimization problems
• Globally convergent Newton-type methods for multiobjective optimization
• Proximal gradient methods for multiobjective optimization and their applications
• On the choice of parameters for the weighting method in vector optimization
• Direct Multisearch for Multiobjective Optimization
• A Derivative-Free Approach to Constrained Multiobjective Nonsmooth Optimization
• Newton's Method for Multiobjective Optimization
• An Adaptive Scalarization Method in Multiobjective Optimization
• Benchmarking optimization software with performance profiles.
• Pareto front approximation through a multi-objective augmented Lagrangian method
• Twenty years of continuous multiobjective optimization in the twenty-first century

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