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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
Fast Parallel Algorithms for Statistical Subset Selection ProblemsCode0
Flex-Net: A Graph Neural Network Approach to Resource Management in Flexible Duplex NetworksCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
Curriculum learning for multilevel budgeted combinatorial problemsCode0
Futureproof Static Memory PlanningCode0
Differentiating Through Integer Linear Programs with Quadratic Regularization and Davis-Yin SplittingCode0
DeciLS-PBO: an Effective Local Search Method for Pseudo-Boolean OptimizationCode0
Curriculum Learning for Cumulative Return MaximizationCode0
Exploratory Combinatorial Optimization with Reinforcement LearningCode0
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