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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 381390 of 1277 papers

TitleStatusHype
Dynamic Algorithms for Matroid Submodular Maximization0
Dynamic Anisotropic Smoothing for Noisy Derivative-Free Optimization0
Deep memetic models for combinatorial optimization problems: application to the tool switching problem0
Dynamic Feature Selection for Dependency Parsing0
A Tutorial about Random Neural Networks in Supervised Learning0
Dynamic Feature Selection for Efficient and Interpretable Human Activity Recognition0
Amplitude-Ensemble Quantum-Inspired Tabu Search Algorithm for Solving 0/1 Knapsack Problems0
Biased Random-Key Genetic Algorithms: A Review0
Dynamic Submodular Maximization0
A Data-Driven Column Generation Algorithm For Bin Packing Problem in Manufacturing Industry0
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