# Bucketized Active Sampling for Learning ACOPF Source: [Bucketized Active Sampling for Learning ACOPF]() Collection index: [llms.txt]() Data updated: 2025-10-25T17:24:25.895Z Authors: Michael Klamkin, Mathieu Tanneau, Terrence W.K. Mak, Pascal Van Hentenryck Published: 2022-08-16T02:04:17.000Z ## Abstract This paper considers optimization proxies for Optimal Power Flow (OPF), i.e., machine-learning models that approximate the input/output relationship of OPF. Recent work has focused on showing that such proxies can be of high fidelity. However, their training requires significant data, each instance necessitating the (offline) solving of an OPF. To meet the requirements of market-clearing applications, this paper proposes Bucketized Active Sampling (BAS), a novel active learning framework that aims at training the best possible OPF proxy within a time limit. BAS partitions the input domain into buckets and uses an acquisition function to determine where to sample next. By applying the same partitioning to the validation set, BAS leverages labeled validation samples in the selection of unlabeled samples. BAS also relies on an adaptive learning rate that increases and decreases over time. Experimental results demonstrate the benefits of BAS. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Logical Reasoning on Logical Reasoning Datasets Unbalanced](): Logical reasoning; Logical Reasoning Datasets Unbalanced; Recolr: 93. - [Logical Reasoning on Logical Reasoning Datasets Balanced](): Logical reasoning; Logical Reasoning Datasets Balanced; Recolr: 91.1. ## Original sources and implementation [Original paper]() [PDF]()