Dense Matrix Functions
For a detailed description of MPI Capability see Dense Matrix Parallelism Using MPI.
Several decompositions and functions required for numerical linear algebra follow. The convention of enclosing optional quantities in brackets, “[ ]” is used.
Defined Array Functions
Matrix Operation
S=SVD(A [,U=U, V=V])
A = USVT
E=EIG(A [[,B=B, D=D],
V=V, W=W])
(AV = VE), AVD = BVE
(AW = WE), AWD = BWE
R=CHOL(A)
A = RT R
Q=ORTH(A [,R=R])
(A = QR), QTQ = I
U=UNIT(A)
[u1] = [a1/a1]
F=DET(A)
Det(A) = determinant
K=RANK(A)
rank(A) = rank
P=NORM(A[,[type=]i])
C=COND(A)
s1 / srank(A)
Z=EYE(N)
Z = IN
A=DIAG(X)
A = diag(x1)
X=DIAGONALS(A)
x = (a11)
Y=FFT (X,[WORK=W]); X=IFFT(Y,[WORK=W])
Discrete Fourier Transform, Inverse
Y=FFT_BOX (X,[WORK=W]); X=IFFT_BOX(Y,[WORK=W])
Discrete Fourier Transform for Boxes, Inverse
A=RAND(A)
Random numbers, 0 < A < 1
L=isNaN(A)
Test for NaN, if (l) then
In certain functions, the optional arguments are inputs while other optional arguments are outputs. To illustrate the example of the box SVD function, a code is given that computes the singular value decomposition and the reconstruction of the random matrix box, A, using the computed factors, R = USVT. Mathematically R = A, but this will be true, only approximately, due to rounding errors. The value units_of_error = A  R/(A∥ɛ), shows the merit of this approximation.
Published date: 03/19/2020
Last modified date: 03/19/2020