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Mixed Integer Nonlinear Programming
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Many engineering, operations, and scientific applications include a mixtureof discrete and continuous decision variables and nonlinear relationshipsinvolving the decision variables that have a pronounced effect on the setof feasible and optimal solutions. Mixed-integer nonlinear programming(MINLP) problems combine the numerical difficulties of handling nonlin-ear functions with the challenge of optimizing in the context of nonconvexfunctions and discrete variables. MINLP is one of the most flexible model-ing paradigms available for optimization; but because its scope is so broad,in the most general cases it is hopelessly intractable. Nonetheless, an ex-panding body of researchers and practitioners including chemical en-gineers, operations researchers, industrial engineers, mechanical engineers,economists, statisticians, computer scientists, operations managers, andmathematical programmers are interested in solving large-scale MINLPinstances.
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