On Learning to Solve Cardinality Constrained Combinatorial Optimization in One-Shot: A Re-parameterization Approach via Gumbel-Sinkhorn-TopK
Cardinality constrained combinatorial optimization requires selecting an optimal subset of $k$ elements, and it will be appealing to design data-driven algorithms that perform TopK selection over a probability distribution predicted by a neural network. However, the existing differentiable TopK operator suffers from an unbounded gap between the soft prediction and the discrete solution, leading to inaccurate estimation of the combinatorial objective score. In this paper, we present a self-supervised learning pipeline for cardinality constrained combinatorial optimization, which incorporates with Gumbel-Sinkhorn-TopK (GS-TopK) for near-discrete TopK predictions and the re-parameterization trick resolving the non-differentiable challenge. Theoretically, we characterize a bounded gap between the Maximum-A-Posteriori (MAP) inference and our proposed method, resolving the divergence issue in the previous differentiable TopK operator and also providing a more accurate estimation of the objective score given a provable tightened bound to the discrete decision variables. Experiments on max covering and discrete clustering problems show that our method outperforms state-of-the-art Gurobi solver and the novel one-shot learning method Erdos Goes Neural.
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