1,846 research outputs found
A Backward Stable Algorithm for Computing the CS Decomposition via the Polar Decomposition
We introduce a backward stable algorithm for computing the CS decomposition
of a partitioned matrix with orthonormal columns, or a
rank-deficient partial isometry. The algorithm computes two polar
decompositions (which can be carried out in parallel) followed by an
eigendecomposition of a judiciously crafted Hermitian matrix. We
prove that the algorithm is backward stable whenever the aforementioned
decompositions are computed in a backward stable way. Since the polar
decomposition and the symmetric eigendecomposition are highly amenable to
parallelization, the algorithm inherits this feature. We illustrate this fact
by invoking recently developed algorithms for the polar decomposition and
symmetric eigendecomposition that leverage Zolotarev's best rational
approximations of the sign function. Numerical examples demonstrate that the
resulting algorithm for computing the CS decomposition enjoys excellent
numerical stability
A sixth-order iterative method for approximating the polar decomposition of an arbitrary matrix
[EN] A new iterative method for computing the polar decomposition of any rectangular complex matrix is presented and analyzed. The study of the convergence shows that this method has order of convergence six. Some numerical tests confirm the theoretical results and allow us to compare the proposed iterative scheme with other known ones. (C) 2015 Elsevier B.V. All rights reserved.This research was partially supported by Ministerio de Economia y Competitividad MTM2014-52016-C2-2-P.Cordero Barbero, A.; Torregrosa Sánchez, JR. (2017). A sixth-order iterative method for approximating the polar decomposition of an arbitrary matrix. Journal of Computational and Applied Mathematics. 318:591-598. https://doi.org/10.1016/j.cam.2015.12.006S59159831
Designing structured tight frames via an alternating projection method
Tight frames, also known as general Welch-bound- equality sequences, generalize orthonormal systems. Numerous applications - including communications, coding, and sparse approximation- require finite-dimensional tight frames that possess additional structural properties. This paper proposes an alternating projection method that is versatile enough to solve a huge class of inverse eigenvalue problems (IEPs), which includes the frame design problem. To apply this method, one needs only to solve a matrix nearness problem that arises naturally from the design specifications. Therefore, it is the fast and easy to develop versions of the algorithm that target new design problems. Alternating projection will often succeed even if algebraic constructions are unavailable. To demonstrate that alternating projection is an effective tool for frame design, the paper studies some important structural properties in detail. First, it addresses the most basic design problem: constructing tight frames with prescribed vector norms. Then, it discusses equiangular tight frames, which are natural dictionaries for sparse approximation. Finally, it examines tight frames whose individual vectors have low peak-to-average-power ratio (PAR), which is a valuable property for code-division multiple-access (CDMA) applications. Numerical experiments show that the proposed algorithm succeeds in each of these three cases. The appendices investigate the convergence properties of the algorithm
The complex step method for approximating the Fréchet derivative of matrix functions in automorphism groups
We show, that the Complex Step approximation to the Fréchet derivative of matrix functions is applicable to the matrix sign, square root and polar mapping using iterative schemes. While this property was already discovered for the matrix sign using Newtons method, we extend the research to the family of Padé iterations, that allows us to introduce iterative schemes for finding function and derivative values while approximately preserving automorphism group structure
Conditioning of Leverage Scores and Computation by QR Decomposition
The leverage scores of a full-column rank matrix A are the squared row norms
of any orthonormal basis for range(A). We show that corresponding leverage
scores of two matrices A and A + \Delta A are close in the relative sense, if
they have large magnitude and if all principal angles between the column spaces
of A and A + \Delta A are small. We also show three classes of bounds that are
based on perturbation results of QR decompositions. They demonstrate that
relative differences between individual leverage scores strongly depend on the
particular type of perturbation \Delta A. The bounds imply that the relative
accuracy of an individual leverage score depends on: its magnitude and the
two-norm condition of A, if \Delta A is a general perturbation; the two-norm
condition number of A, if \Delta A is a perturbation with the same norm-wise
row-scaling as A; (to first order) neither condition number nor leverage score
magnitude, if \Delta A is a component-wise row-scaled perturbation. Numerical
experiments confirm the qualitative and quantitative accuracy of our bounds.Comment: This version has been accepted to SIMAX but has not yet gone through
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Locally Unitarily Invariantizable NEPv and Convergence Analysis of SCF
We consider a class of eigenvector-dependent nonlinear eigenvalue problems
(NEPv) without the unitary invariance property. Those NEPv commonly arise as
the first-order optimality conditions of a particular type of optimization
problems over the Stiefel manifold, and previously, special cases have been
studied in the literature. Two necessary conditions, a definiteness condition
and a rank-preserving condition, on an eigenbasis matrix of the NEPv that is a
global optimizer of the associated optimization problem are revealed, where the
definiteness condition has been known for the special cases previously
investigated. We show that, locally close to the eigenbasis matrix satisfying
both necessary conditions, the NEPv can be reformulated as a unitarily
invariant NEPv, the so-called aligned NEPv, through a basis alignment operation
-- in other words, the NEPv is locally unitarily invariantizable. Numerically,
the NEPv is naturally solved by an SCF-type iteration. By exploiting the
differentiability of the coefficient matrix of the aligned NEPv, we establish a
closed-form local convergence rate for the SCF-type iteration and analyze its
level-shifted variant. Numerical experiments confirm our theoretical results.Comment: 38 pages, 11 figure
Finding a low-rank basis in a matrix subspace
For a given matrix subspace, how can we find a basis that consists of
low-rank matrices? This is a generalization of the sparse vector problem. It
turns out that when the subspace is spanned by rank-1 matrices, the matrices
can be obtained by the tensor CP decomposition. For the higher rank case, the
situation is not as straightforward. In this work we present an algorithm based
on a greedy process applicable to higher rank problems. Our algorithm first
estimates the minimum rank by applying soft singular value thresholding to a
nuclear norm relaxation, and then computes a matrix with that rank using the
method of alternating projections. We provide local convergence results, and
compare our algorithm with several alternative approaches. Applications include
data compression beyond the classical truncated SVD, computing accurate
eigenvectors of a near-multiple eigenvalue, image separation and graph
Laplacian eigenproblems
Multilinear Factorizations for Multi-Camera Rigid Structure from Motion Problems
Camera networks have gained increased importance in recent years. Existing approaches mostly use point correspondences between different camera views to calibrate such systems. However, it is often difficult or even impossible to establish such correspondences. But even without feature point correspondences between different camera views, if the cameras are temporally synchronized then the data from the cameras are strongly linked together by the motion correspondence: all the cameras observe the same motion. The present article therefore develops the necessary theory to use this motion correspondence for general rigid as well as planar rigid motions. Given multiple static affine cameras which observe a rigidly moving object and track feature points located on this object, what can be said about the resulting point trajectories? Are there any useful algebraic constraints hidden in the data? Is a 3D reconstruction of the scene possible even if there are no point correspondences between the different cameras? And if so, how many points are sufficient? Is there an algorithm which warrants finding the correct solution to this highly non-convex problem? This article addresses these questions and thereby introduces the concept of low-dimensional motion subspaces. The constraints provided by these motion subspaces enable an algorithm which ensures finding the correct solution to this non-convex reconstruction problem. The algorithm is based on multilinear analysis, matrix and tensor factorizations. Our new approach can handle extreme configurations, e.g. a camera in a camera network tracking only one single point. Results on synthetic as well as on real data sequences act as a proof of concept for the presented insight
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