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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 601–625 of 1277 papers

TitleStatusHype
Intertwining CP and NLP: The Generation of Unreasonably Constrained Sentences—0
Assortment Planning with Sponsored Products—0
Investigating layer-selective transfer learning of QAOA parameters for Max-Cut problem—0
Ising-based Consensus Clustering on Specialized Hardware—0
Iterated Tabu Search Algorithm for Packing Unequal Circles in a Circle—0
Iterated two-phase local search for the Set-Union Knapsack Problem—0
Heuristic with elements of tabu search for Truck and Trailer Routing Problem—0
JDRec: Practical Actor-Critic Framework for Online Combinatorial Recommender System—0
Continuous Tensor Relaxation for Finding Diverse Solutions in Combinatorial Optimization Problems—0
Continuous Latent Search for Combinatorial Optimization—0
Archive-based Single-Objective Evolutionary Algorithms for Submodular Optimization—0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model—0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning—0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications—0
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures—0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach—0
Kalman Filter Based Multiple Person Head Tracking—0
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization—0
Heed the Noise in Performance Evaluations in Neural Architecture Search—0
Knowledge engineering mixed-integer linear programming: constraint typology—0
Hardness of Online Sleeping Combinatorial Optimization Problems—0
Learning to Reduce Search Space for Generalizable Neural Routing Solver—0
Mathematical Models and Reinforcement Learning based Evolutionary Algorithm Framework for Satellite Scheduling Problem—0
Bayesian Meta-Prior Learning Using Empirical Bayes—0
Hamiltonian-based Quantum Reinforcement Learning for Neural Combinatorial Optimization—0
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