An adaptive, multivariate partitioning algorithm for global optimization of nonconvex programs

Harsha Nagarajan (), Mowen Lu (), Site Wang (), Russell Bent () and Kaarthik Sundar ()
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Harsha Nagarajan: Los Alamos National Laboratory
Mowen Lu: Clemson University
Site Wang: Clemson University
Russell Bent: Los Alamos National Laboratory
Kaarthik Sundar: Los Alamos National Laboratory

Journal of Global Optimization, 2019, vol. 74, issue 4, No 4, 639-675

Abstract: Abstract In this work, we develop an adaptive, multivariate partitioning algorithm for solving nonconvex, Mixed-Integer Nonlinear Programs (MINLPs) with polynomial functions to global optimality. In particular, we present an iterative algorithm that exploits piecewise, convex relaxation approaches via disjunctive formulations to solve MINLPs that is different than conventional spatial branch-and-bound approaches. The algorithm partitions the domains of variables in an adaptive and non-uniform manner at every iteration to focus on productive areas of the search space. Furthermore, domain reduction techniques based on sequential, optimization-based bound-tightening and piecewise relaxation techniques, as a part of a presolve step, are integrated into the main algorithm. Finally, we demonstrate the effectiveness of the algorithm on well-known benchmark problems (including Pooling and Blending instances) from MINLPLib and compare our algorithm with state-of-the-art global optimization solvers. With our novel approach, we solve several large-scale instances, some of which are not solvable by state-of-the-art solvers. We also succeed in reducing the best known optimality gap for a hard, generalized pooling problem instance.

Keywords: Global optimization; Adaptive partitioning; MILP-based methods; Mixed-integer nonlinear programs; McCormick; Piecewise relaxations; Sequential optimization-based bound-tightening (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (8)

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DOI: 10.1007/s10898-018-00734-1

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