Algorithmic differentiation for piecewise smooth functions: a case study for robust optimization

DOI10.1080/10556788.2017.1333613zbMath1401.90168OpenAlexW2626638040MaRDI QIDQ4685597

Andrea Walther, Sabrina Fiege, Kshitij Kulshreshtha, Andreas Griewank

Publication date: 9 October 2018

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2017.1333613

zbMATH Keywords

nonsmooth optimizationrobust optimizationalgorithmic differentiationpiecewise linearization

Mathematics Subject Classification ID

Minimax problems in mathematical programming (90C47) Nonconvex programming, global optimization (90C26) Nonlinear programming (90C30)

Related Items

A survey of nonlinear robust optimization, Relaxing Kink Qualifications and Proving Convergence Rates in Piecewise Smooth Optimization, Derivative-free robust optimization by outer approximations, LiPsMin, On the abs-polynomial expansion of piecewise smooth functions

Uses Software

• ADOL-C
• GradSamp
• HANSO
• PBNCGC
• MPBNGC

Cites Work

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• Nonsmooth optimization via quasi-Newton methods
• Solving piecewise linear systems in ABS-normal form
• First- and second-order optimality conditions for piecewise smooth objective functions
• On stable piecewise linearization and generalized algorithmic differentiation
• Introduction to Piecewise Differentiable Equations
• Limited memory bundle method for large bound constrained nonsmooth optimization: convergence analysis
• Evaluating Derivatives
• A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization