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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 181190 of 1277 papers

TitleStatusHype
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
Learning to Solve Combinatorial Optimization under Positive Linear Constraints via Non-Autoregressive Neural NetworksCode1
Quantum approximate optimization via learning-based adaptive optimizationCode1
Exact Combinatorial Optimization with Graph Convolutional Neural NetworksCode1
Balans: Multi-Armed Bandits-based Adaptive Large Neighborhood Search for Mixed-Integer Programming ProblemCode1
Equivariant quantum circuits for learning on weighted graphsCode1
ML4CO-KIDA: Knowledge Inheritance in Dataset AggregationCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Rationales for Sequential PredictionsCode1
Variational Annealing on Graphs for Combinatorial OptimizationCode1
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