Entropy-Based Optimization of Nonlinear Separable Discrete Decision Models

• Published in: Management science Vol. 60; no. 3; pp. 695 - 707
• Main Authors: Nakagawa, Yuji, James, Ross J. W., Rego, César, Edirisinghe, Chanaka
• Format: Journal Article
• Language: English
• Published: Linthicum INFORMS 01.03.2014; Institute for Operations Research and the Management Sciences
• Subjects: Aggregation; Algorithms; Analysis; Capital budgeting; Combinatorial optimization; Computational methods; Conceptualization; Decision making; Decision making models; Decision models; Decision theory; Entropy; Entropy (Information theory); Information theory; Integers; Knapsack problem; Management science; Mathematical optimization; Methods; multidimensional nonlinear knapsack; Non-linear models; Nonlinear systems; Objective functions; Optimal solutions; Optimization; Optimization algorithms; Packing problem; problem difficulty estimation; Problem sets; separable discrete optimization; Studies; surrogate constraints; Variables; United States
• ISSN: 0025-1909; 1526-5501
• DOI: 10.1287/mnsc.2013.1772

This paper develops a new way to help solve difficult linear and nonlinear discrete-optimization decision models more efficiently by introducing a problem-difficulty metric that uses the concept of entropy from information theory. Our entropy metric is employed to devise rules for problem partitioning within an implicit enumeration method, where branching is accomplished based on the subproblem complexity. The only requirement for applying our metric is the availability of (upper) bounds on branching subproblems, which are often computed within most implicit enumeration methods such as branch-and-bound (or cutting-plane-based) methods. Focusing on problems with a relatively small number of constraints, but with a large number of variables, we develop a hybrid partitioning and enumeration solution scheme by combining the entropic approach with the recently developed improved surrogate constraint (ISC) method to produce the new method we call ISCENT. Our computational results indicate that ISCENT can be an order of magnitude more efficient than commercial solvers, such as CPLEX, for convex instances with no more than eight constraints. Furthermore, for nonconvex instances, ISCENT is shown to be significantly more efficient than other standard global solvers. This paper was accepted by Dimitris Bertsimas, optimization.