# NAG CL Interfacef08anc (zgels)

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## 1Purpose

f08anc solves linear least squares problems of the form
 $minx ‖b-Ax‖2 or minx ‖b-AHx‖2 ,$
where $A$ is an $m×n$ complex matrix of full rank, using a $QR$ or $LQ$ factorization of $A$.

## 2Specification

 #include
 void f08anc (Nag_OrderType order, Nag_TransType trans, Integer m, Integer n, Integer nrhs, Complex a[], Integer pda, Complex b[], Integer pdb, NagError *fail)
The function may be called by the names: f08anc, nag_lapackeig_zgels or nag_zgels.

## 3Description

The following options are provided:
1. 1.If ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ and $m\ge n$: find the least squares solution of an overdetermined system, i.e., solve the least squares problem
 $minx ‖b-Ax‖2 .$
2. 2.If ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ and $m: find the minimum norm solution of an underdetermined system $Ax=b$.
3. 3.If ${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$ and $m\ge n$: find the minimum norm solution of an undetermined system ${A}^{\mathrm{H}}x=b$.
4. 4.If ${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$ and $m: find the least squares solution of an overdetermined system, i.e., solve the least squares problem
 $minx ‖b-AHx‖2 .$
Several right-hand side vectors $b$ and solution vectors $x$ can be handled in a single call; they are stored as the columns of the $m×r$ right-hand side matrix $B$ and the $n×r$ solution matrix $X$.

## 4References

Anderson E, Bai Z, Bischof C, Blackford S, Demmel J, Dongarra J J, Du Croz J J, Greenbaum A, Hammarling S, McKenney A and Sorensen D (1999) LAPACK Users' Guide (3rd Edition) SIAM, Philadelphia https://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore

## 5Arguments

1: $\mathbf{order}$Nag_OrderType Input
On entry: the order argument specifies the two-dimensional storage scheme being used, i.e., row-major ordering or column-major ordering. C language defined storage is specified by ${\mathbf{order}}=\mathrm{Nag_RowMajor}$. See Section 3.1.3 in the Introduction to the NAG Library CL Interface for a more detailed explanation of the use of this argument.
Constraint: ${\mathbf{order}}=\mathrm{Nag_RowMajor}$ or $\mathrm{Nag_ColMajor}$.
2: $\mathbf{trans}$Nag_TransType Input
On entry: if ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$, the linear system involves $A$.
If ${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$, the linear system involves ${A}^{\mathrm{H}}$.
Constraint: ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ or $\mathrm{Nag_ConjTrans}$.
3: $\mathbf{m}$Integer Input
On entry: $m$, the number of rows of the matrix $A$.
Constraint: ${\mathbf{m}}\ge 0$.
4: $\mathbf{n}$Integer Input
On entry: $n$, the number of columns of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.
5: $\mathbf{nrhs}$Integer Input
On entry: $r$, the number of right-hand sides, i.e., the number of columns of the matrices $B$ and $X$.
Constraint: ${\mathbf{nrhs}}\ge 0$.
6: $\mathbf{a}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array a must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pda}}×{\mathbf{n}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}×{\mathbf{pda}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
The $\left(i,j\right)$th element of the matrix $A$ is stored in
• ${\mathbf{a}}\left[\left(j-1\right)×{\mathbf{pda}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{a}}\left[\left(i-1\right)×{\mathbf{pda}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: the $m×n$ matrix $A$.
On exit: if ${\mathbf{m}}\ge {\mathbf{n}}$, a is overwritten by details of its $QR$ factorization, as returned by f08asc.
If ${\mathbf{m}}<{\mathbf{n}}$, a is overwritten by details of its $LQ$ factorization, as returned by f08avc.
7: $\mathbf{pda}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraints:
• if ${\mathbf{order}}=\mathrm{Nag_ColMajor}$, ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$;
• if ${\mathbf{order}}=\mathrm{Nag_RowMajor}$, ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
8: $\mathbf{b}\left[\mathit{dim}\right]$Complex Input/Output
Note: the dimension, dim, of the array b must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdb}}×{\mathbf{nrhs}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}},{\mathbf{n}}\right)×{\mathbf{pdb}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
The $\left(i,j\right)$th element of the matrix $B$ is stored in
• ${\mathbf{b}}\left[\left(j-1\right)×{\mathbf{pdb}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{b}}\left[\left(i-1\right)×{\mathbf{pdb}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: the matrix $B$ of right-hand side vectors, stored in rows or columns; b is $m×r$ if ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$, or $n×r$ if ${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$.
On exit: b is overwritten by the solution vectors, $x$, stored in rows or columns:
• if ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ and $m\ge n$, or ${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$ and $m, elements $1$ to $\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ in each column of b contain the least squares solution vectors; the residual sum of squares for the solution is given by the sum of squares of the modulus of elements $\left(\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)+1\right)$ to $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ in that column;
• otherwise, elements $1$ to $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(m,n\right)$ in each column of b contain the minimum norm solution vectors.
9: $\mathbf{pdb}$Integer Input
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraints:
• if ${\mathbf{order}}=\mathrm{Nag_ColMajor}$, ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}},{\mathbf{n}}\right)$;
• if ${\mathbf{order}}=\mathrm{Nag_RowMajor}$, ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
10: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
NE_BAD_PARAM
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_FULL_RANK
Diagonal element $⟨\mathit{\text{value}}⟩$ of the triangular factor of $A$ is zero, so that $A$ does not have full rank; the least squares solution could not be computed.
NE_INT
On entry, ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{m}}\ge 0$.
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 0$.
On entry, ${\mathbf{nrhs}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{nrhs}}\ge 0$.
On entry, ${\mathbf{pda}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pda}}>0$.
On entry, ${\mathbf{pdb}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdb}}>0$.
NE_INT_2
On entry, ${\mathbf{pda}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}}\right)$.
On entry, ${\mathbf{pda}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry, ${\mathbf{pdb}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{nrhs}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
NE_INT_3
On entry, ${\mathbf{pdb}}=⟨\mathit{\text{value}}⟩$, ${\mathbf{m}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{m}},{\mathbf{n}}\right)$.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
See Section 7.5 in the Introduction to the NAG Library CL Interface for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 8 in the Introduction to the NAG Library CL Interface for further information.

## 7Accuracy

See Section 4.5 of Anderson et al. (1999) for details of error bounds.

## 8Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f08anc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f08anc makes calls to BLAS and/or LAPACK routines, which may be threaded within the vendor library used by this implementation. Consult the documentation for the vendor library for further information.
Please consult the X06 Chapter Introduction for information on how to control and interrogate the OpenMP environment used within this function. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

## 9Further Comments

The total number of floating-point operations required to factorize $A$ is approximately $\frac{8}{3}{n}^{2}\left(3m-n\right)$ if $m\ge n$ and $\frac{8}{3}{m}^{2}\left(3n-m\right)$ otherwise. Following the factorization the solution for a single vector $x$ requires $\mathit{O}\left(\mathrm{min}\phantom{\rule{0.125em}{0ex}}\left({m}^{2},{n}^{2}\right)\right)$ operations.
The real analogue of this function is f08aac.

## 10Example

This example solves the linear least squares problem
 $minx ‖b-Ax‖2 ,$
where
 $A = ( 0.96-0.81i -0.03+0.96i -0.91+2.06i -0.05+0.41i -0.98+1.98i -1.20+0.19i -0.66+0.42i -0.81+0.56i 0.62-0.46i 1.01+0.02i 0.63-0.17i -1.11+0.60i -0.37+0.38i 0.19-0.54i -0.98-0.36i 0.22-0.20i 0.83+0.51i 0.20+0.01i -0.17-0.46i 1.47+1.59i 1.08-0.28i 0.20-0.12i -0.07+1.23i 0.26+0.26i )$
and
 $b = ( -2.09+1.93i 3.34-3.53i -4.94-2.04i 0.17+4.23i -5.19+3.63i 0.98+2.53i ) .$
The square root of the residual sum of squares is also output.

### 10.1Program Text

Program Text (f08ance.c)

### 10.2Program Data

Program Data (f08ance.d)

### 10.3Program Results

Program Results (f08ance.r)