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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

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Showing 1051–1060 of 1277 papers

TitleStatusHype
Combinatorial Keyword Recommendations for Sponsored Search with Deep Reinforcement Learning—0
Differentiable Combinatorial Losses through Generalized Gradients of Linear Programs—0
Combinatorial Network Optimization with Unknown Variables: Multi-Armed Bandits with Linear Rewards—0
Combinatorial optimization and reasoning with graph neural networks—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
Combinatorial optimization for low bit-width neural networks—0
Combinatorial optimization solving by coherent Ising machines based on spiking neural networks—0
Combinatorial Optimization via LLM-driven Iterated Fine-tuning—0
Combinatorial Persistency Criteria for Multicut and Max-Cut—0
Combinatorial Pure Exploration with Full-bandit Feedback and Beyond: Solving Combinatorial Optimization under Uncertainty with Limited Observation—0
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