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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 671680 of 1277 papers

TitleStatusHype
Annealed Training for Combinatorial Optimization on Graphs0
Differentially Private Partial Set Cover with Applications to Facility Location0
Bayesian Optimization for Macro Placement0
Supplementing Recurrent Neural Networks with Annealing to Solve Combinatorial Optimization ProblemsCode0
Reinforcement Learning Assisted Recursive QAOACode0
Neural Topological Ordering for Computation Graphs0
Joint Ranging and Phase Offset Estimation for Multiple Drones using ADS-B Signatures0
A conditional gradient homotopy method with applications to Semidefinite Programming0
Attention Round for Post-Training Quantization0
Learning the Quality of Machine Permutations in Job Shop Scheduling0
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