pymanopt.github.io

Pymanopt

Pymanopt is a Python toolbox for optimization on manifolds, that computes gradients and Hessians automatically. It builds upon the Matlab toolbox Manopt. But is otherwise independent of it. Pymanopt aims to lower the barriers for users wishing to use state of the art techniques for optimization on manifolds, by relying on automatic differentiation for computing gradients and Hessians, saving users time and saving them from potential calculation and implementiation errors. Instantiate a Pymanopt solver.

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Pymanopt | pymanopt.github.io Reviews

<META> DESCRIPTION

Pymanopt is a Python toolbox for optimization on manifolds, that computes gradients and Hessians automatically. It builds upon the Matlab toolbox Manopt. But is otherwise independent of it. Pymanopt aims to lower the barriers for users wishing to use state of the art techniques for optimization on manifolds, by relying on automatic differentiation for computing gradients and Hessians, saving users time and saving them from potential calculation and implementiation errors. Instantiate a Pymanopt solver.

<META> KEYWORDS

1 pymanopt

2 gitter

3 installation

4 dependencies

5 theano

6 or tensorflow

7 installing pymanopt

8 manifolds

9 manifold

10 euclidean manifold

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pymanopt,gitter,installation,dependencies,theano,or tensorflow,installing pymanopt,manifolds,manifold,euclidean manifold,symmetric matrices,symmetric n k=1,sphere manifold,shape,stiefel manifold,grassmann manifold,oblique manifold,oblique m n,for details

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Pymanopt | pymanopt.github.io Reviews

https://pymanopt.github.io

Pymanopt is a Python toolbox for optimization on manifolds, that computes gradients and Hessians automatically. It builds upon the Matlab toolbox Manopt. But is otherwise independent of it. Pymanopt aims to lower the barriers for users wishing to use state of the art techniques for optimization on manifolds, by relying on automatic differentiation for computing gradients and Hessians, saving users time and saving them from potential calculation and implementiation errors. Instantiate a Pymanopt solver.

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pymanopt.github.io

1

pymanopt. — Pymanopt dev documentation

http://pymanopt.github.io/doc

Module containing pymanopt problem class. Use this to build a problem object to feed to one of the solvers. Problem class for setting up a problem to feed to one of the pymanopt solvers. Manifold to optimize over. Grad(x) is the gradient of cost at x. This must take an element X of manifold and return an element of the tangent space to manifold at X. This is usually computed automatically and doesn’t need to be set by the user. Level of information printed by the solver while it operates, 0 is silent, 2 ...

2

MoG

http://pymanopt.github.io/MoG.html

Riemannian Optimisation with Pymanopt for Inference in MoG models ¶. The Mixture of Gaussians (MoG) model assumes that datapoints $ mathbf{x} i in mathbb{R} d$ follow a distribution described by the following probability density function:. Inline # Number of data. For each component, generate all needed samples. Indices of current component in X. Number of those occurrences. Given a data sample the de facto standard method to infer the parameters is the expectation maximisation. Mathcal{PD} {(d 1) times(...

3

pymanopt. — Pymanopt dev documentation

http://pymanopt.github.io/doc/index.html

Module containing pymanopt problem class. Use this to build a problem object to feed to one of the solvers. Problem class for setting up a problem to feed to one of the pymanopt solvers. Manifold to optimize over. Grad(x) is the gradient of cost at x. This must take an element X of manifold and return an element of the tangent space to manifold at X. This is usually computed automatically and doesn’t need to be set by the user. Level of information printed by the solver while it operates, 0 is silent, 2 ...

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pymanopt.github.io

Pymanopt

Pymanopt is a Python toolbox for optimization on manifolds, that computes gradients and Hessians automatically. It builds upon the Matlab toolbox Manopt. But is otherwise independent of it. Pymanopt aims to lower the barriers for users wishing to use state of the art techniques for optimization on manifolds, by relying on automatic differentiation for computing gradients and Hessians, saving users time and saving them from potential calculation and implementiation errors. Instantiate a Pymanopt solver.

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