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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 781790 of 1277 papers

TitleStatusHype
Spatial Field Reconstruction and Sensor Selection in Heterogeneous Sensor Networks with Stochastic Energy Harvesting0
Low-rank combinatorial optimization and statistical learning by spatial photonic Ising machine0
Statistical estimation for optimization problems on graphs0
Stochastic Online Greedy Learning with Semi-bandit Feedbacks0
Storage placement policy for minimizing frequency deviation: A combinatorial optimization approach0
Story-oriented Image Selection and Placement0
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks0
STRCMP: Integrating Graph Structural Priors with Language Models for Combinatorial Optimization0
Streaming Algorithms for News and Scientific Literature Recommendation: Submodular Maximization with a d-Knapsack Constraint0
Streaming, Distributed Variational Inference for Bayesian Nonparametrics0
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