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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 651–675 of 1277 papers

TitleStatusHype
Understanding Curriculum Learning in Policy Optimization for Online Combinatorial OptimizationCode0
L0Learn: A Scalable Package for Sparse Learning using L0 RegularizationCode1
Instance-wise algorithm configuration with graph neural networksCode1
Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Exploring the Feature Space of TSP Instances Using Quality Diversity—0
Yordle: An Efficient Imitation Learning for Branch and Bound—0
MGNN: Graph Neural Networks Inspired by Distance Geometry ProblemCode0
Equivariant neural networks for recovery of Hadamard matrices—0
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
What's Wrong with Deep Learning in Tree Search for Combinatorial OptimizationCode1
The First AI4TSP Competition: Learning to Solve Stochastic Routing ProblemsCode1
Classical Simulation of Variational Quantum Classifiers using Tensor Rings—0
Recent Advances in Deep Learning for Routing Problems—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement LearningCode1
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation—0
Supervised Permutation Invariant Networks for Solving the CVRP with Bounded Fleet Size—0
Neural combinatorial optimization beyond the TSP: Existing architectures under-represent graph structure—0
A General Framework for Evaluating Robustness of Combinatorial Optimization Solvers on Graphs—0
Reconstructing Compact Building Models from Point Clouds Using Deep Implicit FieldsCode1
An Efficient Combinatorial Optimization Model Using Learning-to-Rank DistillationCode0
DeepGANTT: A Scalable Deep Learning Scheduler for Backscatter Networks—0
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