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Jan Kronqvist(jakronqvabo.fi) Abstract: Here we present a centercut algorithm for convex mixedinteger nonlinear programming (MINLP) that can either be used as a primal heuristic or as a deterministic solution technique. Like many other algorithms for convex MINLP, the centercut algorithm constructs a linear approximation of the original problem. The main idea of the algorithm is to use the linear approximation differently in order to find feasible solutions within only a few iterations. The algorithm chooses trial solutions as the center of the current linear outer approximation of the nonlinear constraints, making the trial solutions more likely to satisfy the constraints. The ability to find feasible solutions within only a few iterations makes the algorithm well suited as a primal heuristic, and we prove that the algorithm finds the optimal solution within a finite number of iterations. Numerical results show that the algorithm obtains feasible solutions quickly and is able to obtain good solutions. Keywords: Convex MINLP, cutting plane techniques, centercut algorithm, primal heuristics, outer approximation Category 1: Integer Programming ((Mixed) Integer Nonlinear Programming ) Citation: Preprint of submitted manuscript. Download: [PDF] Entry Submitted: 02/06/2018 Modify/Update this entry  
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