混合整数线性规划（MILP）中的折扣伪成本
Discounted Pseudocosts in MILP
AI总结：
受强化学习折扣总回报启发，研究者提出MILP中的折扣伪成本概念，将前瞻性视角引入伪成本估计，初步实验显示其可改进分支策略、加快难MILP问题求解。
摘要：
本文受强化学习中的折扣总回报启发，提出折扣伪成本（discounted pseudocosts）概念，并探究其在混合整数线性规划（MILP）中的应用。传统伪成本用于估计分支定界过程中变量界变化导致的目标函数变化。通过融合强化学习理念，我们提出一种将前瞻性视角纳入伪成本估计的新方法。文中阐述了折扣伪成本的提出动机，讨论了其如何表示MILP问题空间中经过一层探索后分支的预期回报。在MIPLIB 2017基准算例上的初步实验表明，折扣伪成本有望改进分支策略，加快高难度MILP问题的求解过程。
英文摘要：
In this article, we introduce the concept of discounted pseudocosts, inspired by discounted total reward in reinforcement learning, and explore their application in mixed-integer linear programming (MILP). Traditional pseudocosts estimate changes in the objective function due to variable bound changes during the branch-and-bound process. By integrating reinforcement learning concepts, we propose a novel approach incorporating a forward-looking perspective into pseudocost estimation. We present the motivation behind discounted pseudocosts and discuss how they represent the anticipated reward for branching after one level of exploration in the MILP problem space. Initial experiments on MIPLIB 2017 benchmark instances demonstrate the potential of discounted pseudocosts to enhance branching strategies and accelerate the solution process for challenging MILP problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
