Shape Theory via QR decomposition

This work sets the non isotropic noncentral elliptical shape distributions via QR decomposition in the context of zonal polynomials, avoiding the invariant polynomials and the open problems for their computation. The new shape distributions are easily computable and then the inference procedure can be studied under exact densities instead under the published approximations and asymptotic densities under isotropic models. An application in Biology is studied under the classical gaussian approach and a two non gaussian models.Comment: 13 page

Improving Fixed-Point Implementation of QR Decomposition by Rounding-to-Nearest

QR decomposition is a key operation in many current communication systems. This paper shows how to reduce the area of a fixed-point QR decomposition implementation based on Givens rotations by using a new number representation system. This new representation allows performing round-tonearest at the same cost of truncation. Consequently, the rounding errors of the results are halved, which allows it to reduce the word-length by one bit. This reduction positively impacts on the area, delay and power consumption of the design.Ministry of Education and Science of Spain and Junta of Andalucía under contracts TIN2013-42253-P and TIC-1692, respectively, and Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A low complexity multi-layered space frequency coding detection algorithm for MIMO-OFDM

A low complexity multi-layered space frequency OFDM (MLSF-OFDM) coding scheme is presented with the proposed two detection algorithms, fast QR decomposition detection algorithm or denoted as FAST-QR and enhanced FAST-QR (E-FAST-QR). Both algorithms not only reduce the implementation complexity of QR decomposition but also show a good performance in terms of bit error rate (BER). Hence, the proposed detection algorithms can be used to maintain guaranteed quality of service (QoS) in MIMO-OFDM system

GR decompositions and their relations to Cholesky-like factorizations

For a given matrix, we are interested in computing GR decompositions $A=GR$, where $G$ is an isometry with respect to given scalar products. The orthogonal QR decomposition is the representative for the Euclidian scalar product. For a signature matrix, a respective factorization is given as the hyperbolic QR decomposition. Considering a skew-symmetric matrix leads to the symplectic QR decomposition. The standard approach for computing GR decompositions is based on the successive elimination of subdiagonal matrix entries. For the hyperbolic and symplectic case, this approach does in general not lead to a satisfying numerical accuracy. An alternative approach computes the QR decomposition via a Cholesky factorization, but also has bad stability. It is improved by repeating the procedure a second time. In the same way, the hyperbolic and the symplectic QR decomposition are related to the $LDL^T$ and a skew-symmetric Cholesky-like factorization. We show that methods exploiting this connection can provide better numerical stability than elimination-based approaches

Direct QR factorizations for tall-and-skinny matrices in MapReduce architectures

The QR factorization and the SVD are two fundamental matrix decompositions with applications throughout scientific computing and data analysis. For matrices with many more rows than columns, so-called "tall-and-skinny matrices," there is a numerically stable, efficient, communication-avoiding algorithm for computing the QR factorization. It has been used in traditional high performance computing and grid computing environments. For MapReduce environments, existing methods to compute the QR decomposition use a numerically unstable approach that relies on indirectly computing the Q factor. In the best case, these methods require only two passes over the data. In this paper, we describe how to compute a stable tall-and-skinny QR factorization on a MapReduce architecture in only slightly more than 2 passes over the data. We can compute the SVD with only a small change and no difference in performance. We present a performance comparison between our new direct TSQR method, a standard unstable implementation for MapReduce (Cholesky QR), and the classic stable algorithm implemented for MapReduce (Householder QR). We find that our new stable method has a large performance advantage over the Householder QR method. This holds both in a theoretical performance model as well as in an actual implementation

An algorithm for computing the QR decomposition of a polynomial matrix

This paper introduces an algorithm for computing a QR decomposition of a polynomial matrix. The algorithm proceeds to perform the decomposition by following the same strategy in eliminating entries of the matrix as is used in the Givens method for a QR decomposition of a scalar matrix. However scalar Givens rotation matrices can no longer be applied. Instead, a polynomial Givens rotation is introduced, enabling the QR decomposition of a polynomial matrix. Convergence of the algorithm is discussed and through simulations the capability of the algorithm is assessed