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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 481490 of 1277 papers

TitleStatusHype
Attention-based Reinforcement Learning for Combinatorial Optimization: Application to Job Shop Scheduling Problem0
Quantum-Inspired Machine Learning for Molecular Docking0
Decentralizing Coordination in Open Vehicle Fleets for Scalable and Dynamic Task Allocation0
Simulation Based Bayesian OptimizationCode0
Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition0
Graph Q-Learning for Combinatorial Optimization0
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids0
OsmLocator: locating overlapping scatter marks with a non-training generative perspectiveCode0
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem0
A Unified Pre-training and Adaptation Framework for Combinatorial Optimization on Graphs0
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