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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 11511160 of 1277 papers

TitleStatusHype
Dynamic Submodular Maximization0
EALG: Evolutionary Adversarial Generation of Language Model-Guided Generators for Combinatorial Optimization0
Early years of Biased Random-Key Genetic Algorithms: A systematic review0
EB-GLS: An Improved Guided Local Search Based on the Big Valley Structure0
Effective anytime algorithm for multiobjective combinatorial optimization problems0
Effective Features of Remote Sensing Image Classification Using Interactive Adaptive Thresholding Method0
Efficient 3D Endfiring TRUS Prostate Segmentation with Globally Optimized Rotational Symmetry0
Efficient Algorithms for Adversarial Contextual Learning0
Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack0
Decomposed Quadratization: Efficient QUBO Formulation for Learning Bayesian Network0
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