一类非凸约束二次规划问题的分支定界算法
Branch-and-bound algorithm for non-convex constrained quadratic programming problems
彭明丽 1刘庆怀 1张鸿洋1
作者信息
- 1. 长春工业大学 数学与统计学院,吉林 长春 130012
- 折叠
摘要
针对一类非凸约束二次规划问题,提出一种新的参数化线性松弛分支定界算法,主要利用线性松弛技术求得原问题的全局最优值下界,以及区域删除规则缩减不可行区域,证明了算法的收敛性,最后通过数值实验表明算法的收敛速度加快,且该算法有效可行.
Abstract
The article introduces a novel parameterized linear relaxation branch-and-bound algorithm for a class of non-convex constrained quadratic programming problems.This algorithm primarily utilizes linear relaxation techniques to obtain the global optimal lower bounds of the original problem and employs region deletion rules to reduce the infeasible region.The convergence of the algorithm is proven,and numerical experiments demonstrate its accelerated convergence rate,indicating its effectiveness and feasibility.
关键词
分支定界/线性松弛技术/区域缩减/二次规划Key words
branch and bound/linear relaxation technique/area reduction/quadratic programming引用本文复制引用
基金项目
吉林省自然科学基金面上项目(20101597)
出版年
2024
NETL
NSTL
万方数据