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Algorithms and software for convex mixed integer nonlinear programs. (English) Zbl 1242.90121
Lee, Jon (ed.) et al., Mixed integer nonlinear programming. Selected papers based on the presentations at the IMA workshop mixed-integer nonlinear optimization: Algorithmic advances and applications, Minneapolis, MN, USA, November 17–21, 2008. New York, NY: Springer (ISBN 978-1-4614-1926-6/hbk; 978-1-4614-1927-3/ebook). The IMA Volumes in Mathematics and its Applications 154, 1-39 (2012).
Summary: This paper provides a survey of recent progress and software for solving convex Mixed Integer Nonlinear Programs (MINLP)s, where the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in recent years. By exploiting analogies to well-known techniques for solving Mixed Integer Linear Programs and incorporating these techniques into software, significant improvements have been made in the ability to solve these problems.
For the entire collection see [Zbl 1230.90005].

MSC:
90C11 Mixed integer programming
90C25 Convex programming
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