Manopt, a Matlab Toolbox for Optimization on Manifolds
Authors: Nicolas Boumal, Bamdev Mishra, P.-A. Absil, Rodolphe Sepulchre
JMLR 2014 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. By dealing internally with most of the diļ¬erential geometry, the package aims particularly at lowering the entrance barrier. |
| Researcher Affiliation | Academia | Department of Mathematical Engineering, Universit e catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium; Department of Electrical Engineering and Computer Science Universit e de Li ege, B-4000 Li ege, Belgium; Department of Engineering (Control Group) University of Cambridge, Trumpington Street, Cambridge CB2 1PZ, UK |
| Pseudocode | Yes | In Manopt, simple code for this reads (with Y Rn r such that X = Y Y ): % The problem structure hosts a manifold structure as well as function handles % to define the cost function and its derivatives (here provided as Euclidean % derivatives, which will be converted to their Riemannian equivalent). problem.M = elliptopefactory(n, r); problem.cost = @(Y) trace(Y'*L*Y)/4; problem.egrad = @(Y) (L*Y)/2; problem.ehess = @(Y, U) (L*U)/2; % optional % These diagnostics tools help make sure the gradient and Hessian are correct. checkgradient(problem); pause; checkhessian(problem); pause; % Minimize with trust regions, a random initial guess and default options. Y = trustregions(problem); |
| Open Source Code | Yes | The Manopt toolbox, available at www.manopt.org, is a user-friendly, documented piece of software dedicated to simplify experimenting with state of the art Riemannian optimization algorithms. ... Code and documentation are available at www.manopt.org. |
| Open Datasets | No | The paper describes the 'maximum cut problem' as an example but does not mention the use of any specific dataset for experimentation, nor does it provide access information for any dataset. |
| Dataset Splits | No | No dataset is mentioned as being used for experimentation, therefore no dataset split information is provided. |
| Hardware Specification | No | No hardware specifications are provided in the paper. |
| Software Dependencies | No | Manopt, a Matlab Toolbox for Optimization on Manifolds |
| Experiment Setup | No | The paper provides a code example with general instructions like 'Minimize with trust regions, a random initial guess and default options,' but lacks specific hyperparameters or detailed training configurations. |