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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 521530 of 1277 papers

TitleStatusHype
Graph-SCP: Accelerating Set Cover Problems with Graph Neural NetworksCode0
GRASP: Accelerating Shortest Path Attacks via Graph Attention0
Diversity from Human Feedback0
An Edge-Aware Graph Autoencoder Trained on Scale-Imbalanced Data for Traveling Salesman Problems0
Oracle Efficient Algorithms for Groupwise Regret0
Routing Arena: A Benchmark Suite for Neural Routing Solvers0
GenCO: Generating Diverse Designs with Combinatorial Constraints0
Too Big, so Fail? -- Enabling Neural Construction Methods to Solve Large-Scale Routing ProblemsCode0
Controlling Continuous Relaxation for Combinatorial OptimizationCode0
Genetic Engineering Algorithm (GEA): An Efficient Metaheuristic Algorithm for Solving Combinatorial Optimization Problems0
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