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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 691700 of 1277 papers

TitleStatusHype
Combinatorial optimization for low bit-width neural networks0
On the Generalization of Neural Combinatorial Optimization Heuristics0
Neural Improvement Heuristics for Graph Combinatorial Optimization ProblemsCode0
Evolution as a Service: A Privacy-Preserving Genetic Algorithm for Combinatorial Optimization0
Terrain Analysis in StarCraft 1 and 2 as Combinatorial OptimizationCode0
The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 110
An Introduction to Quantum Machine Learning for Engineers0
Neuromimetic Linear Systems -- Resilience and Learning0
LAWS: Look Around and Warm-Start Natural Gradient Descent for Quantum Neural NetworksCode0
Neural Combinatorial Optimization: a New Player in the Field0
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