首页> 外文会议>Integration of AI and OR Techniques in constraint programming for combinatorial optimization problems >Learning and Propagating Lagrangian Variable Bounds for Mixed-Integer Nonlinear Programming
【24h】

Learning and Propagating Lagrangian Variable Bounds for Mixed-Integer Nonlinear Programming

机译:混合整数非线性规划的学习和传播拉格朗日变量界

获取原文
获取原文并翻译 | 示例

摘要

Optimization-based bound tightening (OBBT) is a domain reduction technique commonly used in nonconvex mixed-integer nonlinear programming that solves a sequence of auxiliary linear programs. Each variable is minimized and maximized to obtain the tightest bounds valid for a global linear relaxation. This paper shows how the dual solutions of the auxiliary linear programs can be used to learn what we call Lagrangian variable bound constraints. These are linear inequalities that explain OBBT's domain reductions in terms of the bounds on other variables and the objective value of the incumbent solution. Within a spatial branch-and-bound algorithm, they can be learnt a priori (during OBBT at the root node) and propagated within the search tree at very low computational cost. Experiments with an implementation inside the MINLP solver SCIP show that this reduces the number of branch-and-bound nodes and speeds up solution times.
机译:基于优化的约束紧缩(OBBT)是一种常域求解技术,通常用于求解非连续混合整数非线性程序中的一系列辅助线性程序。将每个变量最小化和最大化,以获得对全局线性松弛有效的最紧密边界。本文展示了如何使用辅助线性程序的对偶解来学习我们所谓的拉格朗日变量界约束。这些是线性不等式,它们根据其他变量的界线和现有解决方案的目标值来解释OBBT的域减少。在空间分支定界算法中,可以先验地学习它们(在根节点进行OBBT期间),并以非常低的计算成本在搜索树中进行传播。在MINLP求解器SCIP中实施的实验表明,这减少了分支和绑定节点的数量,并加快了求解时间。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号