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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 976–1000 of 1277 papers

TitleStatusHype
Neural Graph Matching Network: Learning Lawler's Quadratic Assignment Problem with Extension to Hypergraph and Multiple-graph Matching—0
Learning to Optimize Variational Quantum Circuits to Solve Combinatorial ProblemsCode0
Smart Predict-and-Optimize for Hard Combinatorial Optimization ProblemsCode0
Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression—0
Black-box Combinatorial Optimization using Models with Integer-valued MinimaCode0
Multi-objectivization Inspired Metaheuristics for the Sum-of-the-Parts Combinatorial Optimization Problems—0
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Multidataset Independent Subspace Analysis with Application to Multimodal FusionCode0
Self-Assignment Flows for Unsupervised Data Labeling on Graphs—0
Learning to Order Graph Elements with Application to Multilingual Surface Realization—0
Word-level Textual Adversarial Attacking as Combinatorial OptimizationCode0
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem—0
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs—0
Entity Summarization: State of the Art and Future Challenges—0
Learning chordal extensions—0
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems—0
Context-Aware Online Adaptation of Mixed Reality Interfaces—0
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization—0
How to Evaluate Machine Learning Approaches for Combinatorial Optimization: Application to the Travelling Salesman ProblemCode0
Faster width-dependent algorithm for mixed packing and covering LPs—0
YaoGAN: Learning Worst-case Competitive Algorithms from Self-generated Inputs—0
Solving Packing Problems by Conditional Query Learning—0
DeepSimplex: Reinforcement Learning of Pivot Rules Improves the Efficiency of Simplex Algorithm in Solving Linear Programming Problems—0
Deep Auto-Deferring Policy for Combinatorial Optimization—0
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