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Chapter Contents
Chapter Introduction
NAG Toolbox

NAG Toolbox: nag_lapack_ztpqrt (f08bp)

 Contents

    1  Purpose
    2  Syntax
    7  Accuracy
    9  Example

Purpose

nag_lapack_ztpqrt (f08bp) computes the QR factorization of a complex m+n by n triangular-pentagonal matrix.

Syntax

[a, b, t, info] = f08bp(l, nb, a, b, 'm', m, 'n', n)
[a, b, t, info] = nag_lapack_ztpqrt(l, nb, a, b, 'm', m, 'n', n)

Description

nag_lapack_ztpqrt (f08bp) forms the QR factorization of a complex m+n by n triangular-pentagonal matrix C,
C= A B  
where A is an upper triangular n by n matrix and B is an m by n pentagonal matrix consisting of an m-l by n rectangular matrix B1 on top of an l by n upper trapezoidal matrix B2:
B= B1 B2 .  
The upper trapezoidal matrix B2 consists of the first l rows of an n by n upper triangular matrix, where 0lminm,n. If l=0, B is m by n rectangular; if l=n and m=n, B is upper triangular.
A recursive, explicitly blocked, QR factorization (see nag_lapack_zgeqrt (f08ap)) is performed on the matrix C. The upper triangular matrix R, details of the unitary matrix Q, and further details (the block reflector factors) of Q are returned.
Typically the matrix A or B2 contains the matrix R from the QR factorization of a subproblem and nag_lapack_ztpqrt (f08bp) performs the QR update operation from the inclusion of matrix B1.
For example, consider the QR factorization of an l by n matrix B^ with l<n: B^ = Q^R^ , R^ = R1^ R2^ , where R1^ is l by l upper triangular and R2^ is n-l by n rectangular (this can be performed by nag_lapack_zgeqrt (f08ap)). Given an initial least-squares problem B^ X^ = Y^  where X and Y are l by nrhs matrices, we have R^ X^ = Q^H Y^ .
Now, adding an additional m-l rows to the original system gives the augmented least squares problem
BX=Y  
where B is an m by n matrix formed by adding m-l rows on top of R^ and Y is an m by nrhs matrix formed by adding m-l rows on top of Q^HY^.
nag_lapack_ztpqrt (f08bp) can then be used to perform the QR factorization of the pentagonal matrix B; the n by n matrix A will be zero on input and contain R on output.
In the case where B^ is r by n, rn, R^ is n by n upper triangular (forming A) on top of r-n rows of zeros (forming first r-n rows of B). Augmentation is then performed by adding rows to the bottom of B with l=0.

References

Elmroth E and Gustavson F (2000) Applying Recursion to Serial and Parallel QR Factorization Leads to Better Performance IBM Journal of Research and Development. (Volume 44) 4 605–624
Golub G H and Van Loan C F (2012) Matrix Computations (4th Edition) Johns Hopkins University Press, Baltimore

Parameters

Compulsory Input Parameters

1:     l int64int32nag_int scalar
l, the number of rows of the trapezoidal part of B (i.e., B2).
Constraint: 0lminm,n.
2:     nb int64int32nag_int scalar
The explicitly chosen block-size to be used in the algorithm for computing the QR factorization. See Further Comments for details.
Constraints:
  • nb1;
  • if n>0, nbn.
3:     alda: – complex array
The first dimension of the array a must be at least max1,n.
The second dimension of the array a must be at least max1,n.
The n by n upper triangular matrix A.
4:     bldb: – complex array
The first dimension of the array b must be at least max1,m.
The second dimension of the array b must be at least max1,n.
The m by n pentagonal matrix B composed of an m-l by n rectangular matrix B1 above an l by n upper trapezoidal matrix B2.

Optional Input Parameters

1:     m int64int32nag_int scalar
Default: the first dimension of the array b.
m, the number of rows of the matrix B.
Constraint: m0.
2:     n int64int32nag_int scalar
Default: the first dimension of the array a and the second dimension of the arrays a, b. (An error is raised if these dimensions are not equal.)
n, the number of columns of the matrix B and the order of the upper triangular matrix A.
Constraint: n0.

Output Parameters

1:     alda: – complex array
The first dimension of the array a will be max1,n.
The second dimension of the array a will be max1,n.
The upper triangle stores the corresponding elements of the n by n upper triangular matrix R.
2:     bldb: – complex array
The first dimension of the array b will be max1,m.
The second dimension of the array b will be max1,n.
Details of the unitary matrix Q.
3:     tldt: – complex array
The first dimension of the array t will be nb.
The second dimension of the array t will be n.
Further details of the unitary matrix Q. The number of blocks is b=knb, where k=minm,n and each block is of order nb except for the last block, which is of order k-b-1×nb. For each of the blocks, an upper triangular block reflector factor is computed: T1,T2,,Tb. These are stored in the nb by n matrix T as T=T1|T2||Tb.
4:     info int64int32nag_int scalar
info=0 unless the function detects an error (see Error Indicators and Warnings).

Error Indicators and Warnings

   info<0
If info=-i, argument i had an illegal value. An explanatory message is output, and execution of the program is terminated.

Accuracy

The computed factorization is the exact factorization of a nearby matrix A+E, where
E2 = Oε A2 ,  
and ε is the machine precision.

Further Comments

The total number of floating-point operations is approximately 23 n2 3m-n  if mn or 23 m2 3n-m  if m<n.
The block size, nb, used by nag_lapack_ztpqrt (f08bp) is supplied explicitly through the interface. For moderate and large sizes of matrix, the block size can have a marked effect on the efficiency of the algorithm with the optimal value being dependent on problem size and platform. A value of nb=64minm,n is likely to achieve good efficiency and it is unlikely that an optimal value would exceed 340.
To apply Q to an arbitrary complex rectangular matrix C, nag_lapack_ztpqrt (f08bp) may be followed by a call to nag_lapack_ztpmqrt (f08bq). For example,
[t, c, info] = f08bq('Left','Transpose', nb, a(:,1:min(m,n)), t, c);
forms C=QHC, where C is m+n by p.
To form the unitary matrix Q explicitly set p=m+n, initialize C to the identity matrix and make a call to nag_lapack_ztpmqrt (f08bq) as above.

Example

This example finds the basic solutions for the linear least squares problems
minimize Axi - bi 2 ,   i=1,2  
where b1 and b2 are the columns of the matrix B,
A = 0.96-0.81i -0.03+0.96i -0.91+2.06i -0.05+0.41i -0.98+1.98i -1.20+0.19i -0.66+0.42i -0.81+0.56i 0.62-0.46i 1.01+0.02i 0.63-0.17i -1.11+0.60i -0.37+0.38i 0.19-0.54i -0.98-0.36i 0.22-0.20i 0.83+0.51i 0.20+0.01i -0.17-0.46i 1.47+1.59i 1.08-0.28i 0.20-0.12i -0.07+1.23i 0.26+0.26i   and    
B= -2.09+1.93i 3.26-2.70i 3.34-3.53i -6.22+1.16i -4.94-2.04i 7.94-3.13i 0.17+4.23i 1.04-4.26i -5.19+3.63i -2.31-2.12i 0.98+2.53i -1.39-4.05i .  
A QR factorization is performed on the first 4 rows of A using nag_lapack_zgeqrt (f08ap) after which the first 4 rows of B are updated by applying QT using nag_lapack_zgemqrt (f08aq). The remaining row is added by performing a QR update using nag_lapack_ztpqrt (f08bp); B is updated by applying the new QT using nag_lapack_ztpmqrt (f08bq); the solution is finally obtained by triangular solve using R from the updated QR.
function f08bp_example


fprintf('f08bp example results\n\n');

% Minimize ||Ax - b|| using recursive QR for m-by-n A and m-by-p B

m = int64(6);
n = int64(4);
p = int64(2);

a = [ 0.96 - 0.81i,  -0.03 + 0.96i,  -0.91 + 2.06i,  -0.05 + 0.41i;
     -0.98 + 1.98i,  -1.20 + 0.19i,  -0.66 + 0.42i,  -0.81 + 0.56i;
      0.62 - 0.46i,   1.01 + 0.02i,   0.63 - 0.17i,  -1.11 + 0.60i;
     -0.37 + 0.38i,   0.19 - 0.54i,  -0.98 - 0.36i,   0.22 - 0.20i;
      0.83 + 0.51i,   0.20 + 0.01i,  -0.17 - 0.46i,   1.47 + 1.59i;
      1.08 - 0.28i,   0.20 - 0.12i,  -0.07 + 1.23i,   0.26 + 0.26i];
b = [-2.09 + 1.93i,   3.26-2.70i;
      3.34 - 3.53i,  -6.22+1.16i;
     -4.94 - 2.04i,   7.94-3.13i;
      0.17 + 4.23i,   1.04-4.26i;
     -5.19 + 3.63i,  -2.31-2.12i;
      0.98 + 2.53i,  -1.39-4.05i];

nb = n;
% Compute the QR Factorisation of first n rows of A
[QRn, Tn, info] = f08ap( ...
			 nb,a(1:n,:));

% Compute C = (C1) = (Q^H)*B
[c1, info] = f08aq( ...
		    'Left', 'Conjugate Transpose', QRn, Tn, b(1:n,:));

% Compute least-squares solutions by backsubstitution in R*X = C1
[x, info] = f07ts( ...
		   'Upper', 'No Transpose', 'Non-Unit', QRn, c1);

% Print first n-row solutions
disp('Solution for n rows');
disp(x(1:n,:));

% Add the remaining rows and perform QR update
nb2 = m-n;
l = int64(0);
[R, Q, T, info] = f08bp( ...
			 l, nb2, QRn, a(n+1:m,:));

% Apply orthogonal transformations to C
[c1,c2,info] = f08bq( ...
		      'Left','Conjugate Transpose', l, Q, T, c1, b(n+1:m,:));

% Compute least-squares solutions for first n rows: R*X = C1
[x, info] = f07ts( ...
		   'Upper', 'No transpose', 'Non-Unit', R, c1);
% Print least-squares solutions for all m rows
disp('Least squares solution');
disp(x(1:n,:));

% Compute and print estimates of the square roots of the residual
% sums of squares
for j=1:p
  rnorm(j) = norm(c2(:,j));
end
fprintf('Square roots of the residual sums of squares\n');
fprintf('%12.2e', rnorm);
fprintf('\n');


f08bp example results

Solution for n rows
  -0.5091 - 1.2428i   0.7569 + 1.4384i
  -2.3789 + 2.8651i   5.1727 - 3.6193i
   1.4634 - 2.2064i  -2.6613 + 2.1339i
   0.4701 + 2.6964i  -2.6933 + 0.2724i

Least squares solution
  -0.5044 - 1.2179i   0.7629 + 1.4529i
  -2.4281 + 2.8574i   5.1570 - 3.6089i
   1.4872 - 2.1955i  -2.6518 + 2.1203i
   0.4537 + 2.6904i  -2.7606 + 0.3318i

Square roots of the residual sums of squares
    6.88e-02    1.87e-01

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