LOTUS：一种用于大型两阶段随机规划中双分解的子集启动框架
LOTUS: A Warm-Start Framework for Powering Dual Decomposition in Large-Scale Two-Stage Stochastic Programs
AI总结：
LOTUS通过子集启动框架提升双分解在大型两阶段随机规划中的效率，实现更快的原问题收敛和更优的解质量。
摘要：
解决大型两阶段随机混合整数规划问题在计算上具有挑战性。我们提出LOTUS，一种基于子集的启动框架，旨在在固定时间预算内增强双分解。通过使用有信息的乘数初始化双搜索，LOTUS加速了原问题的收敛，并部分缓解了弱线性规划松弛的影响。通过在生产计划实例上的广泛计算研究，我们显示，在两小时内，LOTUS在45.83%的情况下产生了显著更好的原问题解，而在仅4.17%的情况下被双分解所超越。
英文摘要：
Solving large two-stage stochastic mixed-integer programs is computationally challenging. We propose LOTUS, a subset-based warm-start framework that enhances Dual Decomposition under fixed time budgets. By initializing the dual search with informed multipliers, LOTUS accelerates primal convergence and partially alleviates the impact of weak LP relaxations. Through an extensive computational study on production planning instances, we show that, within two hours, LOTUS yields significantly better primal solutions in 45.83% of cases, while being outperformed by Dual Decomposition in only 4.17%.
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。
