Nonlinear and Geometric Programming – Current Status

Leon Lasdon ()

Annals of Operations Research, 2001, vol. 105, issue 1, 99-107

Abstract: Great strides have been made in nonlinear programming (NLP) in the last 5 years. In smooth NLP, there are now several reliable and efficient codes capable of solving large problems. Most of these implement GRG or SQP methods, and new software using interior point algorithms is under development. NLP software is now much easier to use, as it is interfaced with many modeling systems, including MSC/NASTRAN, and ANSYS for structural problems, GAMS and AMPL for general optimization, Matlab and Mathcad for general mathematical problems, and the widely used Microsoft Excel spreadsheet. For mixed integer problems, branch and bound and outer approximation codes are now available and are coupled to some of the above modeling systems, while search methods like Tabu Search and Genetic algorithms permit combinatorial, nonsmooth, and nonconvex problems to be attacked. Copyright Kluwer Academic Publishers 2001

Keywords: optimization; nonlinear; gradient; constraints; search (search for similar items in EconPapers)
Date: 2001
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DOI: 10.1023/A:1013301515058

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