On Lipschitz optimization based on gray-box piecewise linearization

DOI10.1007/s10107-015-0934-xzbMath1350.49038OpenAlexW1179775547WikidataQ57389574 ScholiaQ57389574MaRDI QIDQ304252

Sabrina Fiege, Torsten Bosse, Andreas Griewank, Andrea Walther

Publication date: 25 August 2016

Published in: Mathematical Programming. Series A. Series B (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10107-015-0934-x

zbMATH Keywords

nonsmooth optimizationbundle methodsABS-normal formalgorithmic differentiationpiecewise linearity

Mathematics Subject Classification ID

Derivative-free methods and methods using generalized derivatives (90C56) Nonsmooth analysis (49J52)

Related Items

Relaxing Kink Qualifications and Proving Convergence Rates in Piecewise Smooth Optimization, Manifold Sampling for Optimizing Nonsmooth Nonconvex Compositions, Computationally relevant generalized derivatives: theory, evaluation and applications, Branch-locking AD techniques for nonsmooth composite functions and nonsmooth implicit functions, Finite convergence of an active signature method to local minima of piecewise linear functions, Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning, Derivative-free optimization methods, First- and second-order optimality conditions for piecewise smooth objective functions, Characterizing and Testing Subdifferential Regularity in Piecewise Smooth Optimization, An algorithm for nonsmooth optimization by successive piecewise linearization, On the abs-polynomial expansion of piecewise smooth functions, Manifold Sampling for $\ell_1$ Nonconvex Optimization, The Structure of Conservative Gradient Fields

Uses Software

• ADOL-C
• PLCP
• SQPlab

Cites Work

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