Abstract
overview Nonlinear Programming (NLP) problems represent some of the most complex optimization problems. Especially when contrasted with LP problems which have dominated the literature in Operations Research. This is because some of the nice properties such as monotonicity and convexity in Linear Programming are not always possible with NLP problems, so many local optimal solutions will exist, not just a single global optimization one. Figure 5.1 illustrates an example problem and the usual local vs. global phenomenon.
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MacGregor Smith, J. (2021). Nonlinear Programming \(\sum _i\sum _j c_{ij}x_ix_j, \ \forall \ (i,j)\). In: Combinatorial, Linear, Integer and Nonlinear Optimization Apps. Springer Optimization and Its Applications, vol 175. Springer, Cham. https://doi.org/10.1007/978-3-030-75801-1_5
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DOI: https://doi.org/10.1007/978-3-030-75801-1_5
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