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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 611620 of 1277 papers

TitleStatusHype
The Fellowship of the Dyson Ring: ACT&Friends' Results and Methods for GTOC 110
Equivariant quantum circuits for learning on weighted graphsCode1
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
On Circuit Depth Scaling For Quantum Approximate Optimization0
Neural Combinatorial Optimization: a New Player in the Field0
A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock PredictionsCode1
BILP-Q: Quantum Coalition Structure GenerationCode1
Multi-objective Pointer Network for Combinatorial OptimizationCode0
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