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Combinatorial Optimization

Combinatorial Optimization is a category of problems which requires optimizing a function over a combination of discrete objects and the solutions are constrained. Examples include finding shortest paths in a graph, maximizing value in the Knapsack problem and finding boolean settings that satisfy a set of constraints. Many of these problems are NP-Hard, which means that no polynomial time solution can be developed for them. Instead, we can only produce approximations in polynomial time that are guaranteed to be some factor worse than the true optimal solution.

Source: Recent Advances in Neural Program Synthesis

Papers

Showing 121130 of 1277 papers

TitleStatusHype
Fast instance-specific algorithm configuration with graph neural network0
Neural Algorithmic Reasoning for Hypergraphs with Looped Transformers0
Convergence and Running Time of Time-dependent Ant Colony Algorithms0
Random-Key Algorithms for Optimizing Integrated Operating Room Scheduling0
Monte Carlo Tree Search for Comprehensive Exploration in LLM-Based Automatic Heuristic DesignCode2
Multiple-gain Estimation for Running Time of Evolutionary Combinatorial Optimization0
Pareto Optimization with Robust Evaluation for Noisy Subset Selection0
Annealing Machine-assisted Learning of Graph Neural Network for Combinatorial Optimization0
Self-Adaptive Ising Machines for Constrained OptimizationCode0
Transfer Learning for Deep-Unfolded Combinatorial Optimization Solver with Quantum Annealer0
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