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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 5160 of 1277 papers

TitleStatusHype
A Large Language Model-Enhanced Q-learning for Capacitated Vehicle Routing Problem with Time Windows0
UniCO: Towards a Unified Model for Combinatorial Optimization Problems0
Primal-dual algorithm for contextual stochastic combinatorial optimization0
Unraveling the Rainbow: can value-based methods schedule?Code0
Entropy-Guided Sampling of Flat Modes in Discrete SpacesCode0
Integrating Column Generation and Large Neighborhood Search for Bus Driver Scheduling with Complex Break Constraints0
Learning to Learn with Quantum Optimization via Quantum Neural Networks0
QAOA Parameter Transferability for Maximum Independent Set using Graph Attention Networks0
Fitness Landscape of Large Language Model-Assisted Automated Algorithm Search0
Application of the Brain Drain Optimization Algorithm to the N-Queens Problem0
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