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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 12211230 of 1277 papers

TitleStatusHype
Destroy and Repair Using Hyper Graphs for RoutingCode0
Constrained optimization under uncertainty for decision-making problems: Application to Real-Time Strategy gamesCode0
Combining Reinforcement Learning and Optimal Transport for the Traveling Salesman ProblemCode0
Positive Semidefinite Matrix Factorization: A Connection with Phase Retrieval and Affine Rank MinimizationCode0
Solving routing problems for multiple cooperative Unmanned Aerial Vehicles using Transformer networks, vol. 122, pp. 106085, 2023Code0
Mining Potentially Explanatory Patterns via Partial SolutionsCode0
FastCover: An Unsupervised Learning Framework for Multi-Hop Influence Maximization in Social NetworksCode0
Balancing Utility and Fairness in Submodular Maximization (Technical Report)Code0
Terrain Analysis in StarCraft 1 and 2 as Combinatorial OptimizationCode0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
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