用估计子问题最优值的方法，高效寻找近似最优解。
Finding Probably Approximate Optimal Solutions by Training to Estimate the Optimal Values of Subproblems
- 训练模型预测子问题的最优目标值，而非直接求解
- 通过期望偏离最优条件的损失函数进行优化
- 适合需要快速近似解的组合优化场景
本文提出一种求解二值变量实值函数最大化的求解器。该求解器基于一种算法，能够估计来自目标函数分布及其子实例的最优目标值。其估计器的训练依赖于一个不等式，使得可使用期望总偏离最优条件的损失函数，而非目标函数本身。因此，该方法无需计算策略值，也无需依赖已求解实例。
原文摘要 · Abstract (English)
The paper is about developing a solver for maximizing a real-valued function of binary variables. The solver relies on an algorithm that estimates the optimal objective-function value of instances from the underlying distribution of objectives and their respective sub-instances. The training of the estimator is based on an inequality that facilitates the use of the expected total deviation from optimality conditions as a loss function rather than the objective-function itself. Thus, it does not calculate values of policies, nor does it rely on solved instances.
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