Solving Large-Scale Nonlinear Programming Problems by Constraint Partitioning

Abstract. In this paper, we present a constraint-partitioning approach for finding local optimal solutions of large-scale mixed-integer nonlinear programming problems (MINLPs). Based on our observation that MINLPs in many engineering applications have highly structured constraints, we propose to partition these MINLPs by their constraints into subproblems, solve each subproblem by an existing solver, and resolve those violated global constraints across the subproblems using our theory of extended saddle points. Constraint partitioning allows many MINLPs that cannot be solved by existing solvers to be solvable because it leads to easier subproblems that are significant relaxations of the original problem. The success of our approach relies on our ability to resolve violated global constraints efficiently, without requiring exhaustive enumerations of variable values in these constraints. We have developed an algorithm for automatically partitioning a large MINLP in order to minimize t...

• Type: Conference
• Year: 2005
• Where: CP
• Authors: Benjamin W. Wah, Yixin Chen
• Keywords: CP 2005, Local Optimal Solutions, Mixed-integer Nonlinear Programming, Programming Languages, Violated Global Constraints