An algorithm to compute the polar decomposition of a 3 × 3 matrix

We propose an algorithm for computing the polar decomposition of a 3 × 3 real matrix that is based on the connection between orthogonal matrices and quaternions. An important application is to 3D transformations in the level 3 Cascading Style Sheets specification used in web browsers. Our algorithm is numerically reliable and requires fewer arithmetic operations than the alternative of computing the polar decomposition via the singular value decomposition

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 $2n \times n$ matrix with orthonormal columns, or a rank-deficient partial isometry. The algorithm computes two $n \times n$ polar decompositions (which can be carried out in parallel) followed by an eigendecomposition of a judiciously crafted $n \times n$ 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

The geometric mean of two matrices from a computational viewpoint

The geometric mean of two matrices is considered and analyzed from a computational viewpoint. Some useful theoretical properties are derived and an analysis of the conditioning is performed. Several numerical algorithms based on different properties and representation of the geometric mean are discussed and analyzed and it is shown that most of them can be classified in terms of the rational approximations of the inverse square root functions. A review of the relevant applications is given

Computing a logarithm of a unitary matrix with general spectrum

We analyze an algorithm for computing a skew-Hermitian logarithm of a unitary matrix. This algorithm is very easy to implement using standard software and it works well even for unitary matrices with no spectral conditions assumed. Certain examples, with many eigenvalues near -1, lead to very non-Hermitian output for other basic methods of calculating matrix logarithms. Altering the output of these algorithms to force an Hermitian output creates accuracy issues which are avoided in the considered algorithm. A modification is introduced to deal properly with the $J$-skew symmetric unitary matrices. Applications to numerical studies of topological insulators in two symmetry classes are discussed.Comment: Added discussion of Floquet Hamiltonian

Solving the "Isomorphism of Polynomials with Two Secrets" Problem for all Pairs of Quadratic Forms

We study the Isomorphism of Polynomial (IP2S) problem with m=2 homogeneous quadratic polynomials of n variables over a finite field of odd characteristic: given two quadratic polynomials (a, b) on n variables, we find two bijective linear maps (s,t) such that b=t . a . s. We give an algorithm computing s and t in time complexity O~(n^4) for all instances, and O~(n^3) in a dominant set of instances. The IP2S problem was introduced in cryptography by Patarin back in 1996. The special case of this problem when t is the identity is called the isomorphism with one secret (IP1S) problem. Generic algebraic equation solvers (for example using Gr\"obner bases) solve quite well random instances of the IP1S problem. For the particular cyclic instances of IP1S, a cubic-time algorithm was later given and explained in terms of pencils of quadratic forms over all finite fields; in particular, the cyclic IP1S problem in odd characteristic reduces to the computation of the square root of a matrix. We give here an algorithm solving all cases of the IP1S problem in odd characteristic using two new tools, the Kronecker form for a singular quadratic pencil, and the reduction of bilinear forms over a non-commutative algebra. Finally, we show that the second secret in the IP2S problem may be recovered in cubic time

Fast computation of spectral projectors of banded matrices

We consider the approximate computation of spectral projectors for symmetric banded matrices. While this problem has received considerable attention, especially in the context of linear scaling electronic structure methods, the presence of small relative spectral gaps challenges existing methods based on approximate sparsity. In this work, we show how a data-sparse approximation based on hierarchical matrices can be used to overcome this problem. We prove a priori bounds on the approximation error and propose a fast algo- rithm based on the QDWH algorithm, along the works by Nakatsukasa et al. Numerical experiments demonstrate that the performance of our algorithm is robust with respect to the spectral gap. A preliminary Matlab implementation becomes faster than eig already for matrix sizes of a few thousand.Comment: 27 pages, 10 figure