BibTeX
@ARTICLE{
         Fiege2018Adf,
       crossref = "Christianson2018Sio",
       author = "Sabrina Fiege and Andrea Walther and Kshitij Kulshreshtha and Andreas Griewank",
       title = "Algorithmic differentiation for piecewise smooth functions: a case study for robust
         optimization",
       journal = "Optimization Methods \& Software",
       volume = "33",
       number = "4--6",
       pages = "1073--1088",
       year = "2018",
       publisher = "Taylor \& Francis",
       doi = "10.1080/10556788.2017.1333613",
       url = "https://doi.org/10.1080/10556788.2017.1333613",
       eprint = "https://doi.org/10.1080/10556788.2017.1333613",
       abstract = "This paper presents a minimization method for Lipschitz continuous, piecewise
         smooth objective functions based on algorithmic differentiation (AD). We assume that all
         nondifferentiabilities are caused by abs, min, and max. The optimization method generates
         successively piecewise linearizations in abs-normal form and solves these local subproblems by
         exploiting the resulting kink structure. Both the generation of the abs-normal form and the
         exploitation of the kink structure are possible due to extensions of standard AD tools. This work
         presents corresponding drivers for the AD tool ADOL-C which are embedded in the nonsmooth solver
         LiPsMin. Finally, minimax problems from robust optimization are considered. Numerical results and a
         comparison of LiPsMin with other nonsmooth optimization methods are discussed.",
       booktitle = "Special issue of Optimization Methods \& Software: Advances in
         Algorithmic Differentiation",
       editor = "Bruce Christianson and Shaun A. Forth and Andreas Griewank"
}