Matrix Algorithms: Volume 1, Basic Decompositions

@inproceedings{Stewart1998MatrixAV,
  title={Matrix Algorithms: Volume 1, Basic Decompositions},
  author={G. W. Stewart},
  year={1998}
}
  • G. Stewart
  • Published 1 December 1998
  • Computer Science
View via Publisher
357 Citations
Highly Influential Citations
24
Background Citations
101
Methods Citations
102

357 Citations

Citation Type

Citation Type

The l1 solution of linear inequalities
  • A. Dax
  • Mathematics
    Comput. Stat. Data Anal.
  • 2006
Formal Methods for High-Performance Linear Algebra Libraries
TLDR
It is the thesis that exposure to elegant (and ugly) programs tends to yield the insights which are necessary if one wishes to produce consistently well-written code.
Randomized ULV Decomposition for Approximating Low-Rank Matrices
  • M. Kaloorazi, Jie Chen
  • Computer Science
    2019 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)
  • 2019
TLDR
This work proposes a low-rank matrix approximation algorithm called Randomized Rank-k ULV (RR-ULV) decomposition, which is computationally efficient, robust, highly accurate, and can also harness modern computational platforms.
A . 2 Hankel Matrices and the Efficient Hankel Matrix-Vector Products
It is well known that if A is a real square nonsingular matrix, then there exists its unique inverse matrix denoted by A . However in general, if A is an m n matrix (m 6D n), then the inverse matrix…
Reduced complexity detection for massive MIMO-OFDM wireless communication systems
TLDR
A novel receiver is proposed for coded massive MIMO-OFDM systems utilizing log-likelihood ratios derived from complex ratio distributions to model the approximate effective noise (AEN) probability density function (PDF) at the output of a zero-forcing equalizer (ZFE).
Extended least trimmed squares estimator in semiparametric regression models with correlated errors
Under a semiparametric regression model, a family of robust estimates for the regression parameter is proposed. The least trimmed squares (LTS) method is a statistical technique for fitting a…
Adjustable iterative soft-output detection for massive MIMO uplink
TLDR
An adjustable iterative detection algorithm based on NSE is introduced to solve the complexity and convergence problems of minimum mean square error detection and an efficient approach to approximately compute the log-likelihood ratios (LLRs) with low-complexity is proposed.
The widely linear quaternion recursive total least squares
TLDR
A widely linear quaternion recursive total least squares (WL-QRTLS) algorithm is introduced for the processing of ℚ-improper processes contaminated by noise and was shown to exhibit superior performance, under perturbations on both input and output signals, to other adaptive filtering of the same class.
Speed up kernel discriminant analysis
TLDR
Spectral Regression Kernel Discriminant Analysis is presented, which casts discriminant analysis into a regression framework, which facilitates both efficient computation and the use of regularization techniques.
Dynamic Supernodes in Sparse Cholesky Update/Downdate and Triangular Solves
TLDR
A dynamic supernodal solver is shown to exceed the performance of the conventional (BLAS-based) superNodal method for solving triangular systems and forms the basis of x = A\b MATLAB when A is sparse and symmetric positive definite.
...
...