An empirical evaluation of a walk-relax-round heuristic for mixed integer convex programs

作者：Huang Kuo Ling; Mehrotra Sanjay^*

来源：Computational Optimization and Applications, 2015, 60(3): 559-585.

DOI：10.1007/s10589-014-9693-5

• Mixed integer convex programs
• Geometric random walk
• Feasibility pump
• Primal heuristic

Recently, a walk-and-round heuristic was proposed by Huang and Mehrotra (Comput Optim Appl, 2012) for generating high quality feasible solutions of mixed integer linear programs. This approach uses geometric random walks on a polyhedral set to sample points in this set. It subsequently rounds these random points using a heuristic, such as the feasibility pump. In this paper, the walk-and-round heuristic is further developed for the mixed integer convex programs (MICPs). Specifically, an outer approximation relaxation step is incorporated. The resulting approach is called a walk-relax-round heuristic. Computational results on problems from the CMU-IBM library show that the points generated from the random walk steps bring additional value. Specifically, the walk-relax-round heuristic using a long step Dikin walk found an optimal solution for 51 out of the 58 MICP test problems. In comparison, the feasibility pump heuristic starting at a continuous relaxation optimum found an optimal solution for 45 test problems. Computational comparisons with a commercial software Cplex 12.1 on mixed integer convex quadratic programs are also given. Our results show that the walk-relax-round heuristic is promising. This may be because the random walk points provide an improved outer approximation of the convex region.

• 出版日期2015-4
• 单位西北大学