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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 11311140 of 1277 papers

TitleStatusHype
On Approximation Guarantees for Greedy Low Rank Optimization0
A Knowledge-Based Approach to Word Sense Disambiguation by distributional selection and semantic features0
Tight Bounds for Bandit Combinatorial Optimization0
A Hybrid Evolutionary Algorithm Based on Solution Merging for the Longest Arc-Preserving Common Subsequence Problem0
Shape Estimation from Defocus Cue for Microscopy Images via Belief Propagation0
Solving Combinatorial Optimization problems with Quantum inspired Evolutionary Algorithm Tuned using a Novel Heuristic Method0
Neural Combinatorial Optimization with Reinforcement LearningCode1
Maximizing Non-Monotone DR-Submodular Functions with Cardinality Constraints0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Recursive Decomposition for Nonconvex Optimization0
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