Mathematical Solution Techniques — The Nonlinear World

Josef Kallrath
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Josef Kallrath: University of Florida

Chapter Chapter 12 in Business Optimization Using Mathematical Programming, 2021, pp 423-446 from Springer

Abstract: Abstract This chapter provides some of the mathematical and algorithmic backgrounds to solve NLP and MINLP problems to local or global optimality. Covering nonlinear, continuous, or mixed integer optimization in great depth is beyond the scope of this book. Therefore only some essential aspects and ideas are introduced and some basics are presented. Readers with further interest are referred to Gill et al. (1981), Spelluci (1993), Burer & Letchford (2012) for a survey on non-convex MINLP, Belotti et al. (2013) on MINLP, and Boukouvala et al. (2016) for advances on global optimization. Special techniques for NLP problems, often used in oil or food industry, such as recursion or sequential linear programming and distributive recursion, have already been covered in Sect. 11.2 .

Date: 2021
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DOI: 10.1007/978-3-030-73237-0_12

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