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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

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Showing 1076–1100 of 1277 papers

TitleStatusHype
Learning to Perform Local Rewriting for Combinatorial OptimizationCode0
Melding the Data-Decisions Pipeline: Decision-Focused Learning for Combinatorial Optimization—0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Learning-based Efficient Graph Similarity Computation via Multi-Scale Convolutional Set MatchingCode0
MRF Optimization with Separable Convex Prior on Partially Ordered Labels—0
Algoritmos Genéticos Aplicado ao Problema de Roteamento de Veículos—0
Parallelization does not Accelerate Convex Optimization: Adaptivity Lower Bounds for Non-smooth Convex Minimization—0
An Iterative Path-Breaking Approach with Mutation and Restart Strategies for the MAX-SAT Problem—0
Towards fully automated protein structure elucidation with NMR spectroscopy—0
An Approximation Algorithm for Risk-averse Submodular Optimization—0
Boosting Combinatorial Problem Modeling with Machine Learning—0
Memory Augmented Policy Optimization for Program Synthesis and Semantic ParsingCode0
Ranked Reward: Enabling Self-Play Reinforcement Learning for Combinatorial OptimizationCode0
Automatic Rank Selection for High-Speed Convolutional Neural Network—0
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization—0
Deep Learning of Graph Matching—0
INGEOTEC at SemEval-2018 Task 1: EvoMSA and μTC for Sentiment Analysis—0
Generic CP-Supported CMSA for Binary Integer Linear Programs—0
A novel channel pruning method for deep neural network compression—0
Safe Element Screening for Submodular Function Minimization—0
Solving the Rubik's Cube Without Human KnowledgeCode0
Evolutionary RL for Container Loading—0
Recent Progress on Graph Partitioning Problems Using Evolutionary Computation—0
A Multi-task Selected Learning Approach for Solving 3D Flexible Bin Packing Problem—0
Regularized Greedy Column Subset Selection—0
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