This dissertation is focused on the development of new cuts and extended formulations for Mixed Integer Nonlinear Programs. We introduce two techniques to give precise charac- terization of general intersection cuts for several classes of MINLP problems with specific structures. We also study the relation between the introduced cuts and some known classes of cutting planes from MILP. Furthermore, we show how an aggregation technique can be easily extended to characterize the convex hull of sets defined by two quadratic or by a conic quadratic and a quadratic inequality. We also computationally evaluate the performance of the introduced cuts and extended formulations on two classes of MINLP problems.
In Chapter 3 we study the generalization of split, k-branch split, and intersection cuts from MILP to MINLP. We propose two simple techniques to derive general intersection cuts for several classes of MINLP problems with specific structures. In particular, we give simple formulas for split cuts for essentially all convex sets described by a single conic quadratic inequality. We also give simple formulas for k-branch split cuts and some general intersection cuts for a wide variety of convex quadratic sets.
In Chapter 4 we study split cuts and extended formulations for MICQP. In particular, we study the relation between Conic MIR (CMIR) cuts [9] and nonlinear split cuts for a class of MICQP problems. We also study an extended formulation for such a class of MICQP and illustrate how the power of an extended formulation can improve the strength of a cutting plane procedure in MINLP.
In Chapter 5 we consider an aggregation technique introduced by Yıldıran [94] to study the convex hull of regions defined by two quadratic or by a conic quadratic and a quadratic inequality. We show how this aggregation technique can be easily extended to yield valid conic quadratic inequalities for the convex hull of sets defined by two quadratic or by a conic
quadratic and a quadratic inequality. We also show that in many cases under additional assumptions, these valid inequalities characterize the convex hull exactly.
In Chapter 6 we computationally evaluate the performance of the introduced linear and nonlinear cuts and extended formulations on two classes of MINLP problems (Closest Vector Problem and Mean-variance Capital Budgeting). We compare the strength of the nonlinear cuts added to the original formulation versus the linear cuts added to an extended formulation.
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