Benders' Decomposition 101: How to Crack Open a Stochastic Program That's Too Big to Swallow Whole
Towards Data Science3537 字 (约 15 分钟)
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Benders decomposition is an effective algorithm for handling large-scale stochastic programming problems by decomposing the problem into master and subproblems to avoid the size explosion of deterministic equivalents, reducing time complexity from O(n^3.5) to more manageable levels.
入选理由：Benders分解通过主问题-子问题迭代框架解决大规模随机规划问题，避免确定性等价形式的规模爆炸
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