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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 671680 of 1277 papers

TitleStatusHype
Quantum-Based Combinatorial Optimization for Optimal Sensor Placement in Civil Structures0
quantum Case-Based Reasoning (qCBR)0
Quantum Computing and AI: Perspectives on Advanced Automation in Science and Engineering0
Quantum evolutionary algorithm for TSP combinatorial optimisation problem0
Quantum-Hybrid Stereo Matching With Nonlinear Regularization and Spatial Pyramids0
Quantum-inspired annealers as Boltzmann generators for machine learning and statistical physics0
Quantum-Inspired Machine Learning for Molecular Docking0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization0
QUBO transformation using Eigenvalue Decomposition0
Quit When You Can: Efficient Evaluation of Ensembles with Ordering Optimization0
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