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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 231–240 of 1277 papers

TitleStatusHype
A Novel Differentiable Loss Function for Unsupervised Graph Neural Networks in Graph Partitioning—0
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
CHARME: A chain-based reinforcement learning approach for the minor embedding problem—0
Annealed Mean Field Descent Is Highly Effective for Quadratic Unconstrained Binary Optimization—0
Combinatorial Optimization for All: Using LLMs to Aid Non-Experts in Improving Optimization Algorithms—0
A Fitness Landscape View on the Tuning of an Asynchronous Master-Worker EA for Nuclear Reactor Design—0
Charged particle tracking with quantum annealing-inspired optimization—0
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