Now showing items 8-19 of 19

• #### Oblique Procrustes Rotations in Factor Analysis ﻿

(Cornell University, 1982-01)
This paper addresses the problem of rotating a factor matrix obliquely to a least squares fit to a target matrix. The target may be fully or partially specified. An iterative computing procedure is presented. Keywords: ...
• #### omputation of the Generalized Singular Value Decomposition Using Mesh-Connected Processors ﻿

(Cornell University, 1983-07)
This paper concerns the systolic array computation of the generalized singular value decomposition. Numerical algorithms for both one and two-dimensional systolic architectures are discussed.
• #### On the Minres Method of Factor Analysis ﻿

(Cornell University, 1981-08)
The minres method is an effective means for estimating factor loadings under the condition that the sum of squares of the off-diagonal residuals be minimized. This paper is addressed to the efficient implementation and ...
• #### Orthogonal Rotation to a Partially Specified Target ﻿

(Cornell University, 1981-09)
This paper addresses the problem of finding an orthogonal transformation of an arbitrary factor solution that would lead to a least squares fit of a partially specified target matrix. An iterative computing procedure is ...
• #### Quadratic Programming with M-Matrices ﻿

(Cornell University, 1979-10)
In this paper, we study the problem of quadratic programming with M-matrices. We describe (1) an effective algorithm for the case where the variables are subject to a lower bound constraint, and (2) an analogous algorithm ...
• #### The Solution of Singular Value Problems Using Systolic Arrays ﻿

(Cornell University, 1984-08)
This paper contains the computation of the singular value decomposition using systolic arrays. Two different linear time algorithms are presented.
• #### The Solution of Singular-Value and Symmetric Eigenvalue Problems on Multiprocessor Arrays ﻿

(Cornell University, 1983-07)
Parallel Jacobi-like algorithms are presented for computing a singular-value decomposition of an $mxn$ matrix $(m \geq n)$ and an eigenvalue decomposition of an $n x n$ symmetric matrix. A linear array of $O(n)$ processors ...
• #### Some Linear-Time Algorithms for Systolic Arrays ﻿

(Cornell University, 1983-01)
We survey some recent results on linear-time and almost linear-time algorithms for one and two-dimensional systolic arrays. In particular, we show how the greatest common divisor (GCD) of two polynomials of degree $n$ ...
• #### A Systolic Architecture for Almost Linear-Time Solution of the Symmetric Eigenvalue Problem ﻿

(Cornell University, 1982-08)
An algorithm is presented for computing the eigenvalues and eigenvectors of an n x n real symmetric matrix. The algorithm is essentially a Jacobi method implemented on a two-dimensional systolic array of $O(n^{2})$ ...
• #### A Systolic Architecture for the Singular Value Decomposition ﻿

(Cornell University, 1982-09)
We propose a systolic architecture for computing a singular value decomposition of an m x n matrix, where $m \geq n$. Our algorithm is stable and requires only $O(mn)$ time on a linear array of $O(n)$ processors. ...
• #### A Systolic Array for the Linear-Time Solution of Toeplitz Systems of Equations ﻿

(Cornell University, 1982-11)
The solution of an (n+1)x(n+1) Toeplitz system of linear equations on a one-dimensional systolic architecture is studied. Our implementation of an algorithm due to Bareiss is shown to require only $O(n)$ time and $O(n)$ ...
• #### A Triangular Processor Array for Computing the Singular Value Decomposition ﻿

(Cornell University, 1984-07)
A triangular processor array for computing a singular value decomposition (SVD) of an $m \times n (m \geq n)$ matrix is proposed. A Jacobi-type algorithm is used to first triangularize the given matrix and then diagonalize ...