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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 931940 of 1277 papers

TitleStatusHype
Ants can orienteer a thief in their robberyCode0
Gumbel-softmax-based Optimization: A Simple General Framework for Optimization Problems on Graphs0
Rethinking Differentiable Search for Mixed-Precision Neural NetworksCode1
A General Large Neighborhood Search Framework for Solving Integer Linear Programs0
Deep Graph Matching via Blackbox Differentiation of Combinatorial SolversCode1
Diseño e implementación de una meta-heurística multi-poblacional de optimización combinatoria enfocada a la resolución de problemas de asignación de rutas a vehículos0
Segmentation and Optimal Region Selection of Physiological Signals using Deep Neural Networks and Combinatorial Optimization0
Real-World Airline Crew Pairing Optimization: Customized Genetic Algorithm versus Column Generation Method0
Reinforcement Learning for Combinatorial Optimization: A Survey0
A Bayesian algorithm for retrosynthesisCode1
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