AI 中文总结
研究通过机器学习自动发现算法，基于储层计算提出方法，用组合优化问题动态规划记录的结果作线性回归特征辅助计算，在旅行商和子集和问题上验证，能提高近似精度、减少计算时间，展现新计算形式。
AI 中文摘要
重用先前计算的结果是降低计算成本的长期原则，但这种重用主要限于单个问题的计算。原则上，跨多个同时求解的问题共享计算过程仍然可行，但手动设计利用非平凡跨任务关系的算法很困难。在此，我们使用机器学习自动发现此类算法。具体而言，基于储层计算，我们提出一种方法，将组合优化问题的动态规划记录的计算结果用作线性回归的特征，利用它们辅助其他组合优化计算。我们在旅行商问题和子集和问题上验证了该方法。复用动态规划过程提高了相对于通用特征的近似精度，并与独立求解相比减少了计算时间。这些结果表明了一种不同于传统计算设计的新计算形式，其中多个过程有效共享和复用中间结果与状态。
英文摘要
Reusing previously computed results is a long-standing principle for reducing computational cost, but such reuse has largely been confined to a single problem's computation. Sharing computational processes across multiple simultaneously solved problems remains possible in principle, yet designing algorithms that exploit nontrivial cross-task relationships is difficult to do manually. Here, we use machine learning to discover such algorithms automatically. Specifically, based on reservoir computing, we propose a method that uses computation results recorded by dynamic programming for combinatorial optimization problems as features for linear regression, leveraging them to assist other combinatorial optimization computations. We validate the approach on the traveling salesman and subset sum problems. Multiplexing the dynamic programming process improves approximation accuracy over generic features and reduces computation time compared with independent solutions. These results suggest a new form of computation, distinct from conventional computational design, in which multiple processes efficiently share and recycle intermediate results and states.
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