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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 176–200 of 1277 papers

TitleStatusHype
Automatic Truss Design with Reinforcement LearningCode1
Efficient Joint Optimization of Layer-Adaptive Weight Pruning in Deep Neural NetworksCode1
Parallel AutoRegressive Models for Multi-Agent Combinatorial OptimizationCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionCode1
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
Equivariant quantum circuits for learning on weighted graphsCode1
RAMA: A Rapid Multicut Algorithm on GPUCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on GraphsCode1
Learning What to Defer for Maximum Independent SetsCode1
Beyond the Heatmap: A Rigorous Evaluation of Component Impact in MCTS-Based TSP SolversCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Belief Propagation Neural NetworksCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
Kernels of Mallows Models under the Hamming Distance for solving the Quadratic Assignment ProblemCode0
Joint Graph Decomposition and Node Labeling: Problem, Algorithms, ApplicationsCode0
Lagrange Oscillatory Neural Networks for Constraint Satisfaction and OptimizationCode0
An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut ProblemCode0
Ants can orienteer a thief in their robberyCode0
Structural Causal Models Reveal Confounder Bias in Linear Program ModellingCode0
Interferometric Neural NetworksCode0
Large Language Model Assisted Adversarial Robustness Neural Architecture SearchCode0
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