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dc.contributor.authorBergmann, Ronny
dc.date.accessioned2023-03-07T19:55:19Z
dc.date.available2023-03-07T19:55:19Z
dc.date.created2022-02-11T07:22:41Z
dc.date.issued2022
dc.identifier.issn2475-9066
dc.identifier.urihttps://hdl.handle.net/11250/3056874
dc.description.abstractManopt.jl provides a set of optimization algorithms for optimization problems given on a Riemannian manifold M. Based on a generic optimization framework, together with the interface ManifoldsBase.jl for Riemannian manifolds, classical and recently developed methods are provided in an efficient implementation. Algorithms include the derivative-free Particle Swarm and Nelder–Mead algorithms, as well as classical gradient, conjugate gradient and stochastic gradient descent. Furthermore, quasi-Newton methods like a Riemannian L-BFGS and nonsmooth optimization algorithms like a Cyclic Proximal Point Algorithm, a (parallel) Douglas-Rachford algorithm and a Chambolle-Pock algorithm are provided, together with several basic cost functions, gradients and proximal maps as well as debug and record capabilities.en_US
dc.description.abstractManopt.jl: Optimization on Manifolds in Juliaen_US
dc.language.isoengen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleManopt.jl: Optimization on Manifolds in Juliaen_US
dc.title.alternativeManopt.jl: Optimization on Manifolds in Juliaen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US
dc.description.versionpublishedVersionen_US
dc.source.volume7en_US
dc.source.journalJournal of Open Source Software (JOSS)en_US
dc.identifier.doi10.21105/joss.03866
dc.identifier.cristin2000275
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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Navngivelse 4.0 Internasjonal
Except where otherwise noted, this item's license is described as Navngivelse 4.0 Internasjonal