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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 451460 of 1277 papers

TitleStatusHype
Self-Improved Learning for Scalable Neural Combinatorial Optimization0
Multi-Robot Connected Fermat Spiral CoverageCode0
Leveraging Constraint Programming in a Deep Learning Approach for Dynamically Solving the Flexible Job-Shop Scheduling Problem0
Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization0
Efficient Combinatorial Optimization via Heat DiffusionCode0
An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem0
FALCON: FLOP-Aware Combinatorial Optimization for Neural Network PruningCode0
Deep Reinforcement Learning for Modelling Protein Complexes0
AcceleratedLiNGAM: Learning Causal DAGs at the speed of GPUsCode0
Graph Learning for Parameter Prediction of Quantum Approximate Optimization Algorithm0
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