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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 851–875 of 1277 papers

TitleStatusHype
FireCommander: An Interactive, Probabilistic Multi-agent Environment for Heterogeneous Robot TeamsCode1
POMO: Policy Optimization with Multiple Optima for Reinforcement LearningCode1
Information-theoretic Feature Selection via Tensor Decomposition and Submodularity—0
Reinforcement Learning with Combinatorial Actions: An Application to Vehicle RoutingCode1
Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problemsCode0
Feature Importance Ranking for Deep LearningCode1
Continuous Latent Search for Combinatorial Optimization—0
Neural Large Neighborhood Search—0
CoCo: Learning Strategies for Online Mixed-Integer Control—0
Virtual Savant: learning for optimization—0
From Local Structures to Size Generalization in Graph Neural Networks—0
Binary matrix factorization on special purpose hardware—0
An Efficient Circuit Compilation Flow for Quantum Approximate Optimization Algorithm—0
AT-MFCGA: An Adaptive Transfer-guided Multifactorial Cellular Genetic Algorithm for Evolutionary Multitasking—0
An Efficient Algorithm for Cooperative Semi-Bandits—0
StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks—0
On Size Generalization in Graph Neural Networks—0
Complex Vehicle Routing with Memory Augmented Neural Networks—0
On combinatorial optimization for dominating sets (literature survey, new models)—0
LeadCache: Regret-Optimal Caching in NetworksCode0
Deep Momentum Uncertainty Hashing—0
Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic ModelsCode0
Cost-aware Feature Selection for IoT Device Classification—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing—0
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