Aggregate subgradient smoothing mehtods for large scale nonsmooth nonconvex optimisation and applications

Title: Aggregate subgradient smoothing mehtods for large scale nonsmooth nonconvex optimisation and applications
Creator: Sultanova, Nargiz
Date: 2015
Type: Text; Journal article
Identifier: http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/89653
Identifier: vital:9300
Identifier: https://doi.org/10.1017/S0004972715000143
Identifier: ISSN:0004-9727
Abstract: Nonsmooth optimisation problems are problems which deal with minimisation or maximisation of functions that are not necessarily differentiable. They arise frequently in many practical applications, for example in engineering, machine learning and economics. In addition, some smooth problems can be reformulated as nonsmooth optimisation problems with a simpler structure or a smaller dimension. Despite the fact that there exist many algorithms for solving nonsmooth optimisation problems, the field is still very much in development. Nonsmooth nonconvex optimisation, in particular, is far from being considered a mature branch of optimisation.
Publisher: Cambridge University Press
Relation: Bulletin of the Australian Mathematical Society Vol. 91, no. 3 (2015), p. 523-524
Rights: This metadata is freely available under a CCO license
Subject: 0101 Pure Mathematics; Large-scale problems; Mathematical programming; Nonsmooth optimisation
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