New method predicts hardness for combinatorial auction problems

A research paper proposes a new approach to solving the Winner Determination Problem (WDP) in combinatorial auctions, which is known to be NP-hard. Instead of trying to replace existing solvers with graph neural networks (GNNs), the paper focuses on predicting when specific instances are too difficult for fast greedy heuristics. The researchers developed a lightweight classifier using a multilayer perceptron (MLP) that predicts the optimality gap of greedy algorithms with high accuracy. For instances identified as hard, a specialized GNN solver is employed, which performs significantly better than greedy methods on adversarial configurations. This hybrid approach, combining the hardness classifier with both GNN and greedy solvers, aims to improve efficiency by selectively deploying more computationally intensive methods. AI

IMPACT This research could lead to more efficient deployment of computational resources for complex optimization problems by intelligently selecting the appropriate algorithm.

RANK_REASON The cluster contains a research paper detailing a novel method for solving a specific computational problem. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• CATS
• combinatorial auctions
• graph neural networks
• Gurobi
• ML-for-combinatorial-optimization
• multilayer perceptron
• Sungwoo Kang
• Winner Determination Problem

• paper
• other

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