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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 981990 of 1277 papers

TitleStatusHype
Approximation Guarantees of Local Search Algorithms via Localizability of Set Functions0
A Probability Density Theory for Spin-Glass Systems0
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation0
A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation0
A Random-Key Optimizer for Combinatorial Optimization0
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization0
A Review of the Family of Artificial Fish Swarm Algorithms: Recent Advances and Applications0
Artificial Catalytic Reactions in 2D for Combinatorial Optimization0
A Simulated Annealing-Based Multiobjective Optimization Algorithm for Minimum Weight Minimum Connected Dominating Set Problem0
A Spectral Method for Unsupervised Multi-Document Summarization0
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