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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 611620 of 1277 papers

TitleStatusHype
Heed the Noise in Performance Evaluations in Neural Architecture Search0
Joint Cluster Head Selection and Trajectory Planning in UAV-Aided IoT Networks by Reinforcement Learning with Sequential Model0
DCILP: A Distributed Approach for Large-Scale Causal Structure Learning0
Joint Graph Decomposition & Node Labeling: Problem, Algorithms, Applications0
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures0
Joint User Pairing and Association for Multicell NOMA: A Pointer Network-based Approach0
Kalman Filter Based Multiple Person Head Tracking0
A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization0
Kernels over Sets of Finite Sets using RKHS Embeddings, with Application to Bayesian (Combinatorial) Optimization0
Hardness of Online Sleeping Combinatorial Optimization Problems0
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