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cs.DS2024
Theoretically Grounded Pruning of Large Ground Sets for Constrained, Discrete Optimization
Ankur Nath, Alan Kuhnle
Modern instances of combinatorial optimization problems often exhibit billion-scale ground sets, which have many uninformative or redundant elements. In this work, we develop light…
cs.AI2024
A Benchmark for Maximum Cut: Towards Standardization of the Evaluation of Learned Heuristics for Combinatorial Optimization
Ankur Nath, Alan Kuhnle
Recently, there has been much work on the design of general heuristics for graph-based, combinatorial optimization problems via the incorporation of Graph Neural Networks (GNNs) to…