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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 581590 of 1277 papers

TitleStatusHype
Simulation-guided Beam Search for Neural Combinatorial OptimizationCode1
Unsupervised Learning for Combinatorial Optimization with Principled Objective RelaxationCode1
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures0
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
Attention Round for Post-Training Quantization0
Learning the Quality of Machine Permutations in Job Shop Scheduling0
A conditional gradient homotopy method with applications to Semidefinite Programming0
The Neural-Prediction based Acceleration Algorithm of Column Generation for Graph-Based Set Covering Problems0
Analyzing the behaviour of D'WAVE quantum annealer: fine-tuning parameterization and tests with restrictive Hamiltonian formulations0
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization0
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