Titlebook: Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming; Theory, Algorithms, Mohit Tawarmalani,Nikol

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Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear ProgrammingTheory, Algorithms,

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Relaxations of Factorable Programs,le in size and can be generated in an automated fashion. To enable the use of efficient LP software, in Section 4.2 we build linear programming relaxations of the nonlinear convex relaxations using the sandwich algorithm (Rote 1992).

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Node Partitioning Schemes,ut it is found to be practically efficient as well. In the second part of this chapter, we study finiteness issues for branch-and-bound. In particular, we develop a finite branching scheme for stochastic two-stage integer programs with pure-integer recourse.

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Miscellaneous Problems,n areas. All problems were solved to global optimality with an absolute tolerance of 10. unless otherwise specified. Our computational experience demonstrates that it is now possible to solve (mixed-integer) nonlinear optimization problems to global optimality in an automated fashion with reasonable computational resources.

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Vorwort und Einführung zum Gesamtwerkle in size and can be generated in an automated fashion. To enable the use of efficient LP software, in Section 4.2 we build linear programming relaxations of the nonlinear convex relaxations using the sandwich algorithm (Rote 1992).

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Vorwort und Einführung zum Gesamtwerk implementing BARON. This chapter also highlights some useful enhancements to the branch-and-bound algorithm. Throughout this chapter, we assume that the problem being solved is a minimization problem.

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1571-568X tationally tractable model at the time. Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing. Modelers have, however, found the assumpt

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