PCA_stochastic | Example of stochastic gradient algorithm in Manopt on a PCA problem. |

dominant_invariant_subspace | Returns an orthonormal basis of the dominant invariant p-subspace of A. |

dominant_invariant_subspace_complex | Returns a unitary basis of the dominant invariant p-subspace of A. |

elliptope_SDP | Solver for semidefinite programs (SDP's) with unit diagonal constraints. |

elliptope_SDP_complex | Solver for complex semidefinite programs (SDP's) with unit diagonal. |

essential_svd | Sample solution of an optimization problem on the essential manifold. |

generalized_eigenvalue_computation | Returns orthonormal basis of the dominant invariant p-subspace of B^-1 A. |

generalized_procrustes | Rotationally align clouds of points (generalized Procrustes problem) |

low_rank_dist_completion | Perform low-rank distance matrix completion w/ automatic rank detection. |

low_rank_matrix_completion | Given partial observation of a low rank matrix, attempts to complete it. |

low_rank_tensor_completion | Given partial observation of a low rank tensor, attempts to complete it. |

maxcut | Algorithm to (try to) compute a maximum cut of a graph, via SDP approach. |

nonlinear_eigenspace | Example of nonlinear eigenvalue problem: total energy minimization. |

packing_on_the_sphere | Return a set of points spread out on the sphere. |

positive_definite_karcher_mean | Computes a Karcher mean of a collection of positive definite matrices. |

radio_interferometric_calibration | Returns the gain matrices of N stations with K receivers. |

robust_pca | Computes a robust version of PCA (principal component analysis) on data. |

shapefit_smoothed | ShapeFit formulation for sensor network localization from pair directions |

sparse_pca | Sparse principal component analysis based on optimization over Stiefel. |

thomson_problem | Simple attempt at computing n well distributed points on a sphere in R^d. |

truncated_svd | Returns an SVD decomposition of A truncated to rank p. |