Benchmarking large-scale distributed convex quadratic programming algorithms

DOI10.1080/10556788.2014.911298zbMath1321.49046OpenAlexW2024728784MaRDI QIDQ2943814

Attila Kozma, Christian Conte, Moritz Diehl

Publication date: 4 September 2015

Published in: Optimization Methods and Software (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10556788.2014.911298

zbMATH Keywords

quadratic programminglarge-scale optimizationdual decompositiondistributed optimization

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Numerical methods involving duality (49M29) Quadratic programming (90C20) Decomposition methods (49M27)

Related Items (3)

A secant-based Nesterov method for convex functions ⋮ Network-decentralised optimisation and control: an explicit saturated solution ⋮ An Augmented Lagrangian Based Algorithm for Distributed NonConvex Optimization

Uses Software

• OpenMPI
• Python

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