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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 461470 of 1277 papers

TitleStatusHype
LayerNAS: Neural Architecture Search in Polynomial Complexity0
Genetic Algorithm Based Combinatorial Optimization for the Optimal Design of Water Distribution Network of Gurudeniya Service Zone, Sri Lanka0
Quantum Annealing for Single Image Super-Resolution0
RELS-DQN: A Robust and Efficient Local Search Framework for Combinatorial Optimization0
Doubly Stochastic Matrix Models for Estimation of Distribution Algorithms0
Combinatorial Optimization enriched Machine Learning to solve the Dynamic Vehicle Routing Problem with Time WindowsCode1
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine0
Quantum approximate optimization via learning-based adaptive optimizationCode1
Graph Neural Networks for the Offline Nanosatellite Task Scheduling ProblemCode0
RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation ResearchCode1
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