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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 10711080 of 1277 papers

TitleStatusHype
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
MRF Optimization with Separable Convex Prior on Partially Ordered Labels0
Algoritmos Genéticos Aplicado ao Problema de Roteamento de Veículos0
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem0
Towards fully automated protein structure elucidation with NMR spectroscopy0
An Approximation Algorithm for Risk-averse Submodular Optimization0
Boosting Combinatorial Problem Modeling with Machine Learning0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
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