1 citations · 1 across the 4 of their papers we have counts for
4 papers
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…
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…
Discretely Beyond : Guided Combinatorial Algorithms for Submodular Maximization
Yixin Chen, Ankur Nath, Chunli Peng +1
For constrained, not necessarily monotone submodular maximization, all known approximation algorithms with ratio greater than require continuous ideas, such as queries to the…
Unveiling the Limits of Learned Local Search Heuristics: Are You the Mightiest of the Meek?
Ankur Nath, Alan Kuhnle
In recent years, combining neural networks with local search heuristics has become popular in the field of combinatorial optimization. Despite its considerable computational demand…