1,630 research outputs found
A Riemannian rank-adaptive method for low-rank matrix completion
The low-rank matrix completion problem can be solved by Riemannian
optimization on a fixed-rank manifold. However, a drawback of the known
approaches is that the rank parameter has to be fixed a priori. In this paper,
we consider the optimization problem on the set of bounded-rank matrices. We
propose a Riemannian rank-adaptive method, which consists of fixed-rank
optimization, rank increase step and rank reduction step. We explore its
performance applied to the low-rank matrix completion problem. Numerical
experiments on synthetic and real-world datasets illustrate that the proposed
rank-adaptive method compares favorably with state-of-the-art algorithms. In
addition, it shows that one can incorporate each aspect of this rank-adaptive
framework separately into existing algorithms for the purpose of improving
performance.Comment: 22 pages, 12 figures, 1 tabl
Guarantees of Riemannian Optimization for Low Rank Matrix Completion
We study the Riemannian optimization methods on the embedded manifold of low
rank matrices for the problem of matrix completion, which is about recovering a
low rank matrix from its partial entries. Assume entries of an
rank matrix are sampled independently and uniformly with replacement. We
first prove that with high probability the Riemannian gradient descent and
conjugate gradient descent algorithms initialized by one step hard thresholding
are guaranteed to converge linearly to the measured matrix provided
\begin{align*} m\geq C_\kappa n^{1.5}r\log^{1.5}(n), \end{align*} where
is a numerical constant depending on the condition number of the
underlying matrix. The sampling complexity has been further improved to
\begin{align*} m\geq C_\kappa nr^2\log^{2}(n) \end{align*} via the resampled
Riemannian gradient descent initialization. The analysis of the new
initialization procedure relies on an asymmetric restricted isometry property
of the sampling operator and the curvature of the low rank matrix manifold.
Numerical simulation shows that the algorithms are able to recover a low rank
matrix from nearly the minimum number of measurements
Geometric methods on low-rank matrix and tensor manifolds
In this chapter we present numerical methods for low-rank matrix and tensor problems that explicitly make use of the geometry of rank constrained matrix and tensor spaces. We focus on two types of problems: The first are optimization problems, like matrix and tensor completion, solving linear systems and eigenvalue problems. Such problems can be solved by numerical optimization for manifolds, called Riemannian optimization methods. We will explain the basic elements of differential geometry in order to apply such methods efficiently to rank constrained matrix and tensor spaces. The second type of problem is ordinary differential equations, defined on matrix and tensor spaces. We show how their solution can be approximated by the dynamical low-rank principle, and discuss several numerical integrators that rely in an essential way on geometric properties that are characteristic to sets of low rank matrices and tensors
Riemannian Optimization for Distance-Geometric Inverse Kinematics
Solving the inverse kinematics problem is a fundamental challenge in motion
planning, control, and calibration for articulated robots. Kinematic models for
these robots are typically parametrized by joint angles, generating a
complicated mapping between the robot configuration and the end-effector pose.
Alternatively, the kinematic model and task constraints can be represented
using invariant distances between points attached to the robot. In this paper,
we formalize the equivalence of distance-based inverse kinematics and the
distance geometry problem for a large class of articulated robots and task
constraints. Unlike previous approaches, we use the connection between distance
geometry and low-rank matrix completion to find inverse kinematics solutions by
completing a partial Euclidean distance matrix through local optimization.
Furthermore, we parametrize the space of Euclidean distance matrices with the
Riemannian manifold of fixed-rank Gram matrices, allowing us to leverage a
variety of mature Riemannian optimization methods. Finally, we show that bound
smoothing can be used to generate informed initializations without significant
computational overhead, improving convergence. We demonstrate that our inverse
kinematics solver achieves higher success rates than traditional techniques,
and substantially outperforms them on problems that involve many workspace
constraints.Comment: 20 pages, 14 figure
A dual framework for low-rank tensor completion
One of the popular approaches for low-rank tensor completion is to use the
latent trace norm regularization. However, most existing works in this
direction learn a sparse combination of tensors. In this work, we fill this gap
by proposing a variant of the latent trace norm that helps in learning a
non-sparse combination of tensors. We develop a dual framework for solving the
low-rank tensor completion problem. We first show a novel characterization of
the dual solution space with an interesting factorization of the optimal
solution. Overall, the optimal solution is shown to lie on a Cartesian product
of Riemannian manifolds. Furthermore, we exploit the versatile Riemannian
optimization framework for proposing computationally efficient trust region
algorithm. The experiments illustrate the efficacy of the proposed algorithm on
several real-world datasets across applications.Comment: Aceepted to appear in Advances of Nueral Information Processing
Systems (NIPS), 2018. A shorter version appeared in the NIPS workshop on
Synergies in Geometric Data Analysis 201
- …