On the convergence of steepest descent methods for multiobjective optimization

DOI10.1007/s10589-020-00192-0zbMath1447.90056OpenAlexW3023935351MaRDI QIDQ782907

Marco Sciandrone, Stefano Lucidi, Giampaolo Liuzzi, Guido Cocchi

Publication date: 29 July 2020

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

Full work available at URL: http://hdl.handle.net/2158/1223219

zbMATH Keywords

multiobjective optimizationsteepest descent algorithma posteriori method

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Multi-objective and goal programming (90C29) Nonlinear programming (90C30) Derivative-free methods and methods using generalized derivatives (90C56)

Related Items (9)

An incremental descent method for multi-objective optimization ⋮ Using first-order information in direct multisearch for multiobjective optimization ⋮ A penalty decomposition approach for multi-objective cardinality-constrained optimization problems ⋮ Approximations for Pareto and proper Pareto solutions and their KKT conditions ⋮ Improved front steepest descent for multi-objective optimization ⋮ Pareto front approximation through a multi-objective augmented Lagrangian method ⋮ Multiobjective BFGS method for optimization on Riemannian manifolds ⋮ A memetic procedure for global multi-objective optimization ⋮ A limited memory quasi-Newton approach for multi-objective optimization

Uses Software

• Gurobi
• PaGMO/PyGMO
• TensorFlow
• ASMO
• MOIF
• DFMO
• PaGMO

Cites Work

• Unnamed Item
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• Direct Multisearch for Multiobjective Optimization
• A Derivative-Free Approach to Constrained Multiobjective Nonsmooth Optimization
• Scalarization in vector optimization
• Network Models and Optimization
• Newton's Method for Multiobjective Optimization
• An Adaptive Scalarization Method in Multiobjective Optimization
• Benchmarking optimization software with performance profiles.

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