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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 12111220 of 1277 papers

TitleStatusHype
Fast instance-specific algorithm configuration with graph neural network0
Feature Selection for Classification with QAOA0
Feature subset selection for Big Data via Chaotic Binary Differential Evolution under Apache Spark0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Feeder Load Balancing using Neural Network0
Fewer Truncations Improve Language Modeling0
Finding and Exploring Promising Search Space for the 0-1 Multidimensional Knapsack Problem0
Finding Support Examples for In-Context Learning0
First-Order Bayesian Regret Analysis of Thompson Sampling0
First-order regret bounds for combinatorial semi-bandits0
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