Analyzing Learned Heuristics for Max-Cut Optimization

Authors: Ankur Nath, Department of Computer Science and Engineering, Texas A&M University; Alan Kuhnle, Department of Computer Science and Engineering, Texas A&M University. Table of Links Abstract & Introduction Related work Evaluation for Max-Cut Evaluation for SAT Summary and Outlook, References Supplementary Materials 3 EVALUATION for MAX-CUT 3.1 Problem Formulation 3.2 Datasets for Max-Cut In this subsection, we briefly discuss datasets included in our analysis.

and S2V-DQN exhibited promising performance across a diverse range of graph structures, including those not present in their training data. We run similar experiments for TS and SoftTabu agents to see if they exhibit weaker generalization performance compared to ECO-DQN and S2V-DQN. In our empirical evaluation, we discover that SoftTabu and TS display similar or even superior performance when compared to all learned heuristics, as illustrated in Figure 2.

demonstrates only marginal improvement. This outcome may be anticipated. When machine learning models are trained on specific datasets, they may learn patterns and heuristics that are tailored to that particular data. However, when presented with unseen or different data , these learned heuristics may not generalize well and could lead to suboptimal performance or poor outcomes.

dataset is extensively used to benchmark SOTA heuristics for MaxCut. The dataset comprises three types of weighted and unweighted random graphs: Erd˝os-R´enyi graphs with uniform edge probabilities, skew graphs with decaying connectivity, and regular toroidal graphs.

Source: The AI Report (theaireport.net)

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