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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 351360 of 1277 papers

TitleStatusHype
Characterization of Locality in Spin States and Forced Moves for Optimizations0
Towards the Inferrence of Structural Similarity of Combinatorial Landscapes0
Pointer Networks Trained Better via Evolutionary Algorithms0
Biased Random-Key Genetic Algorithms: A Review0
A Bayesian approach for prompt optimization in pre-trained language models0
TOP-Former: A Multi-Agent Transformer Approach for the Team Orienteering ProblemCode0
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning0
A Survey and Analysis of Evolutionary Operators for PermutationsCode0
Variational Annealing on Graphs for Combinatorial OptimizationCode1
Exact Combinatorial Optimization with Temporo-Attentional Graph Neural Networks0
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