Heuristics for convex mixed integer nonlinear programs

作     者：Bonami, Pierre Goncalves, Joao P. M. 

作者机构：IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA Aix Marseille Univ CNRS Lab Informat Fondamentale Marseille Marseille France 

出 版 物：《COMPUTATIONAL OPTIMIZATION AND APPLICATIONS》 (计算优化及其应用)

年 卷 期：2012年第51卷第2期

页      面：729-747页

核心收录：

学科分类：1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 070104[理学-应用数学] 0701[理学-数学] 

基　　金：ANR [BLAN06-1-138894] 

主　　题：Mixed integer nonlinear programming Heuristics 

摘      要：In this paper, we describe the implementation of some heuristics for convex mixed integer nonlinear programs. The work focuses on three families of heuristics that have been successfully used for mixed integer linear programs: diving heuristics, the Feasibility Pump, and Relaxation Induced Neighborhood Search (RINS). We show how these heuristics can be adapted in the context of mixed integer nonlinear programming. We present results from computational experiments on a set of instances that show how the heuristics implemented help finding feasible solutions faster than the traditional branch-and-bound algorithm and how they help in reducing the total solution time of the branch-and-bound algorithm.