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 effcient 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 opt...
The optimization of a real-valued objective function f(U), where U is a p X d,p > d, semi-orthogonal...
1.1 What kind of problems HOPT solves This software package provides a collection of MATLAB m-files ...
A principal way of addressing constrained optimization problems is to model them as problems on Riem...
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on ...
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on ...
Optimization on manifolds is a powerful paradigm to address nonlinear optimization problems. It has ...
A presentation of Manopt, our toolbox for Riemannian optimization available at www.manopt.or
How to make the best decision? This general concern, pervasive in both research and industry, is wha...
Riemannian optimization is the task of finding an optimum of a real-valued function defined on a Riema...
We present a geometric optimization approach to approximate solutions of ma- trix equations by low-r...
Manifold optimization appears in a wide variety of computational problems in the applied sciences. I...
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix se...
ABSTRACT. Mathematical models often mvolve differentiable manifolds that are implicitly defined as t...
Summary. This paper provides an introduction to the topic of optimization on manifolds. The approac...
In both academic problems and industrial applications, it is inevitable to encounter some sort of op...
The optimization of a real-valued objective function f(U), where U is a p X d,p > d, semi-orthogonal...
1.1 What kind of problems HOPT solves This software package provides a collection of MATLAB m-files ...
A principal way of addressing constrained optimization problems is to model them as problems on Riem...
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on ...
Optimization on manifolds is a rapidly developing branch of nonlinear optimization. Its focus is on ...
Optimization on manifolds is a powerful paradigm to address nonlinear optimization problems. It has ...
A presentation of Manopt, our toolbox for Riemannian optimization available at www.manopt.or
How to make the best decision? This general concern, pervasive in both research and industry, is wha...
Riemannian optimization is the task of finding an optimum of a real-valued function defined on a Riema...
We present a geometric optimization approach to approximate solutions of ma- trix equations by low-r...
Manifold optimization appears in a wide variety of computational problems in the applied sciences. I...
Many problems in the sciences and engineering can be rephrased as optimization problems on matrix se...
ABSTRACT. Mathematical models often mvolve differentiable manifolds that are implicitly defined as t...
Summary. This paper provides an introduction to the topic of optimization on manifolds. The approac...
In both academic problems and industrial applications, it is inevitable to encounter some sort of op...
The optimization of a real-valued objective function f(U), where U is a p X d,p > d, semi-orthogonal...
1.1 What kind of problems HOPT solves This software package provides a collection of MATLAB m-files ...
A principal way of addressing constrained optimization problems is to model them as problems on Riem...