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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 481490 of 1277 papers

TitleStatusHype
A Memetic Algorithm Based on Breakout Local Search for the Generalized Travelling Salesman Problem0
Generative Neural Network based Spectrum Sharing using Linear Sum Assignment Problems0
Generative Pre-Trained Transformer for Symbolic Regression Base In-Context Reinforcement Learning0
Federated Combinatorial Multi-Agent Multi-Armed Bandits0
Graph Ordering: Towards the Optimal by Learning0
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
Deep Dynamic Attention Model with Gate Mechanism for Solving Time-dependent Vehicle Routing Problems0
DeepDA: LSTM-based Deep Data Association Network for Multi-Targets Tracking in Clutter0
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