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Jiajin Yu(jiajinyugatech.edu) Abstract: Motivated by concave cost combinatorial optimization problems, we study the following mixed integer nonlinear set: P = {(w,x) : w >= f(a'x), e'x <= k, x in {0,1}^n } where f is a concave function, n and k are positive integers, a is a nonnegative vector, e is a vector of ones, and x'y denotes the scalar product of vectors x and y of same dimension. A standard linearization approach for P is to exploit the fact that f(a'x) is submodular with respect to the binary vector x. We extend this approach to take the cardinality constraint e'x <= k into account and provide a full description of the convex hull of P when the vector a has identical components. We also develop a family of facetdefining inequalities when the vector a has nonidentical components. Computational results using the proposed inequalities in a branchandcut framework to solve meanrisk knapsack problems show significant decrease in both time and the number of nodes over standard methods. Keywords: submodular optimization, integer programming, polyhedral theory Category 1: Integer Programming (Cutting Plane Approaches ) Category 2: Integer Programming (01 Programming ) Category 3: Integer Programming ((Mixed) Integer Linear Programming ) Citation: Technical report, ISyE, Georgia Tech Download: [PDF] Entry Submitted: 08/29/2014 Modify/Update this entry  
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