Manopt, a Matlab toolbox for optimization on manifolds
원문 초록
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on problems where the smooth geometry of the search space can be leveraged to design efficient numerical algorithms. In particular, optimization on manifolds is well-suited to deal with rank and orthogonality constraints. Such structured constraints appear pervasively in machine learning applications, including low-rank matrix completion, sensor network localization, camera network registration, independent component analysis, metric learning, dimensionality reduction and so on. 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. We aim particularly at reaching practitioners outside our field.
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Supervised Quadratic Feature Analysis: Information Geometry Approach for Dimensionality Reduction
New Hardness Results for Low-Rank Matrix Completion
AltGDmin: Alternating GD and Minimization for Partly-Decoupled (Federated) Optimization
Truncated Matrix Completion - An Empirical Study
Norm-Bounded Low-Rank Adaptation
Efficient Low-Rank Matrix Factorization based on l1,ε-norm for Online Background Subtraction
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