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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 301310 of 1277 papers

TitleStatusHype
Efficient Combinatorial Optimization via Heat DiffusionCode0
Deep Reinforcement Learning for Modelling Protein Complexes0
Ant Colony Sampling with GFlowNets for Combinatorial OptimizationCode2
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
RouteExplainer: An Explanation Framework for Vehicle Routing ProblemCode1
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm0
Where the Really Hard Quadratic Assignment Problems Are: the QAP-SAT instancesCode0
Learning to Solve Job Shop Scheduling under UncertaintyCode2
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