# NAG Library Function Document

## 1Purpose

nag_zgesvx (f07apc) uses the $LU$ factorization to compute the solution to a complex system of linear equations
 $AX=B or ATX=B or AHX=B ,$
where $A$ is an $n$ by $n$ matrix and $X$ and $B$ are $n$ by $r$ matrices. Error bounds on the solution and a condition estimate are also provided.

## 2Specification

 #include #include
 void nag_zgesvx (Nag_OrderType order, Nag_FactoredFormType fact, Nag_TransType trans, Integer n, Integer nrhs, Complex a[], Integer pda, Complex af[], Integer pdaf, Integer ipiv[], Nag_EquilibrationType *equed, double r[], double c[], Complex b[], Integer pdb, Complex x[], Integer pdx, double *rcond, double ferr[], double berr[], double *recip_growth_factor, NagError *fail)

## 3Description

nag_zgesvx (f07apc) performs the following steps:
1. Equilibration
The linear system to be solved may be badly scaled. However, the system can be equilibrated as a first stage by setting ${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$. In this case, real scaling factors are computed and these factors then determine whether the system is to be equilibrated. Equilibrated forms of the systems $AX=B$, ${A}^{\mathrm{T}}X=B$ and ${A}^{\mathrm{H}}X=B$ are
 $DR A DC DC-1X = DR B ,$
 $DR A DC T DR-1 X = DC B ,$
and
 $DR A DC H DR-1 X = DC B ,$
respectively, where ${D}_{R}$ and ${D}_{C}$ are diagonal matrices, with positive diagonal elements, formed from the computed scaling factors.
When equilibration is used, $A$ will be overwritten by ${D}_{R}A{D}_{C}$ and $B$ will be overwritten by ${D}_{R}B$ (or ${D}_{C}B$ when the solution of ${A}^{\mathrm{T}}X=B$ or ${A}^{\mathrm{H}}X=B$ is sought).
2. Factorization
The matrix $A$, or its scaled form, is copied and factored using the $LU$ decomposition
 $A=PLU ,$
where $P$ is a permutation matrix, $L$ is a unit lower triangular matrix, and $U$ is upper triangular.
This stage can be by-passed when a factored matrix (with scaled matrices and scaling factors) are supplied; for example, as provided by a previous call to nag_zgesvx (f07apc) with the same matrix $A$.
3. Condition Number Estimation
The $LU$ factorization of $A$ determines whether a solution to the linear system exists. If some diagonal element of $U$ is zero, then $U$ is exactly singular, no solution exists and the function returns with a failure. Otherwise the factorized form of $A$ is used to estimate the condition number of the matrix $A$. If the reciprocal of the condition number is less than machine precision then a warning code is returned on final exit.
4. Solution
The (equilibrated) system is solved for $X$ (${D}_{C}^{-1}X$ or ${D}_{R}^{-1}X$) using the factored form of $A$ (${D}_{R}A{D}_{C}$).
5. Iterative Refinement
Iterative refinement is applied to improve the computed solution matrix and to calculate error bounds and backward error estimates for the computed solution.
6. Construct Solution Matrix $X$
If equilibration was used, the matrix $X$ is premultiplied by ${D}_{C}$ (if ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$) or ${D}_{R}$ (if ${\mathbf{trans}}=\mathrm{Nag_Trans}$ or $\mathrm{Nag_ConjTrans}$) so that it solves the original system before equilibration.

## 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 http://www.netlib.org/lapack/lug
Golub G H and Van Loan C F (1996) Matrix Computations (3rd Edition) Johns Hopkins University Press, Baltimore
Higham N J (2002) Accuracy and Stability of Numerical Algorithms (2nd Edition) SIAM, Philadelphia

## 5Arguments

1:    $\mathbf{order}$Nag_OrderTypeInput
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 3.3.1.3 in How to Use the NAG Library and its Documentation for a more detailed explanation of the use of this argument.
Constraint: ${\mathbf{order}}=\mathrm{Nag_RowMajor}$ or $\mathrm{Nag_ColMajor}$.
2:    $\mathbf{fact}$Nag_FactoredFormTypeInput
On entry: specifies whether or not the factorized form of the matrix $A$ is supplied on entry, and if not, whether the matrix $A$ should be equilibrated before it is factorized.
${\mathbf{fact}}=\mathrm{Nag_Factored}$
af and ipiv contain the factorized form of $A$. If ${\mathbf{equed}}\ne \mathrm{Nag_NoEquilibration}$, the matrix $A$ has been equilibrated with scaling factors given by r and c. a, af and ipiv are not modified.
${\mathbf{fact}}=\mathrm{Nag_NotFactored}$
The matrix $A$ will be copied to af and factorized.
${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$
The matrix $A$ will be equilibrated if necessary, then copied to af and factorized.
Constraint: ${\mathbf{fact}}=\mathrm{Nag_Factored}$, $\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$.
3:    $\mathbf{trans}$Nag_TransTypeInput
On entry: specifies the form of the system of equations.
${\mathbf{trans}}=\mathrm{Nag_NoTrans}$
$AX=B$ (No transpose).
${\mathbf{trans}}=\mathrm{Nag_Trans}$
${A}^{\mathrm{T}}X=B$ (Transpose).
${\mathbf{trans}}=\mathrm{Nag_ConjTrans}$
${A}^{\mathrm{H}}X=B$ (Conjugate transpose).
Constraint: ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$, $\mathrm{Nag_Trans}$ or $\mathrm{Nag_ConjTrans}$.
4:    $\mathbf{n}$IntegerInput
On entry: $n$, the number of linear equations, i.e., the order of the matrix $A$.
Constraint: ${\mathbf{n}}\ge 0$.
5:    $\mathbf{nrhs}$IntegerInput
On entry: $r$, the number of right-hand sides, i.e., the number of columns of the matrix $B$.
Constraint: ${\mathbf{nrhs}}\ge 0$.
6:    $\mathbf{a}\left[\mathit{dim}\right]$ComplexInput/Output
Note: the dimension, dim, of the array a must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pda}}×{\mathbf{n}}\right)$.
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 $n$ by $n$ matrix $A$.
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$ and ${\mathbf{equed}}\ne \mathrm{Nag_NoEquilibration}$, a must have been equilibrated by the scaling factors in r and/or c.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$ or $\mathrm{Nag_NotFactored}$, or if ${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$ and ${\mathbf{equed}}=\mathrm{Nag_NoEquilibration}$, a is not modified.
If ${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$ or ${\mathbf{equed}}\ne \mathrm{Nag_NoEquilibration}$, $A$ is scaled as follows:
• if ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$, $A={D}_{R}A$;
• if ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$, $A=A{D}_{C}$;
• if ${\mathbf{equed}}=\mathrm{Nag_RowAndColumnEquilibration}$, $A={D}_{R}A{D}_{C}$.
7:    $\mathbf{pda}$IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array a.
Constraint: ${\mathbf{pda}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
8:    $\mathbf{af}\left[\mathit{dim}\right]$ComplexInput/Output
Note: the dimension, dim, of the array af must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdaf}}×{\mathbf{n}}\right)$.
The $\left(i,j\right)$th element of the matrix is stored in
• ${\mathbf{af}}\left[\left(j-1\right)×{\mathbf{pdaf}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{af}}\left[\left(i-1\right)×{\mathbf{pdaf}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On entry: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, af contains the factors $L$ and $U$ from the factorization $A=PLU$ as computed by nag_zgetrf (f07arc). If ${\mathbf{equed}}\ne \mathrm{Nag_NoEquilibration}$, af is the factorized form of the equilibrated matrix $A$.
If ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$, af need not be set.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$, af returns the factors $L$ and $U$ from the factorization $A=PLU$ of the original matrix $A$.
If ${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$, af returns the factors $L$ and $U$ from the factorization $A=PLU$ of the equilibrated matrix $A$ (see the description of a for the form of the equilibrated matrix).
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$, af is unchanged from entry.
9:    $\mathbf{pdaf}$IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array af.
Constraint: ${\mathbf{pdaf}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
10:  $\mathbf{ipiv}\left[\mathit{dim}\right]$IntegerInput/Output
Note: the dimension, dim, of the array ipiv must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, ipiv contains the pivot indices from the factorization $A=PLU$ as computed by nag_zgetrf (f07arc); at the $i$th step row $i$ of the matrix was interchanged with row ${\mathbf{ipiv}}\left[i-1\right]$. ${\mathbf{ipiv}}\left[i-1\right]=i$ indicates a row interchange was not required.
If ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$, ipiv need not be set.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$, ipiv contains the pivot indices from the factorization $A=PLU$ of the original matrix $A$.
If ${\mathbf{fact}}=\mathrm{Nag_EquilibrateAndFactor}$, ipiv contains the pivot indices from the factorization $A=PLU$ of the equilibrated matrix $A$.
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$, ipiv is unchanged from entry.
11:  $\mathbf{equed}$Nag_EquilibrationType *Input/Output
On entry: if ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$, equed need not be set.
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$, equed must specify the form of the equilibration that was performed as follows:
• if ${\mathbf{equed}}=\mathrm{Nag_NoEquilibration}$, no equilibration;
• if ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$, row equilibration, i.e., $A$ has been premultiplied by ${D}_{R}$;
• if ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$, column equilibration, i.e., $A$ has been postmultiplied by ${D}_{C}$;
• if ${\mathbf{equed}}=\mathrm{Nag_RowAndColumnEquilibration}$, both row and column equilibration, i.e., $A$ has been replaced by ${D}_{R}A{D}_{C}$.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, equed is unchanged from entry.
Otherwise, if no constraints are violated, equed specifies the form of equilibration that was performed as specified above.
Constraint: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, ${\mathbf{equed}}=\mathrm{Nag_NoEquilibration}$, $\mathrm{Nag_RowEquilibration}$, $\mathrm{Nag_ColumnEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$.
12:  $\mathbf{r}\left[\mathit{dim}\right]$doubleInput/Output
Note: the dimension, dim, of the array r must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: if ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$, r need not be set.
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$ and ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, r must contain the row scale factors for $A$, ${D}_{R}$; each element of r must be positive.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, r is unchanged from entry.
Otherwise, if no constraints are violated and ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, r contains the row scale factors for $A$, ${D}_{R}$, such that $A$ is multiplied on the left by ${D}_{R}$; each element of r is positive.
13:  $\mathbf{c}\left[\mathit{dim}\right]$doubleInput/Output
Note: the dimension, dim, of the array c must be at least $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry: if ${\mathbf{fact}}=\mathrm{Nag_NotFactored}$ or $\mathrm{Nag_EquilibrateAndFactor}$, c need not be set.
If ${\mathbf{fact}}=\mathrm{Nag_Factored}$ and ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, c must contain the column scale factors for $A$, ${D}_{C}$; each element of c must be positive.
On exit: if ${\mathbf{fact}}=\mathrm{Nag_Factored}$, c is unchanged from entry.
Otherwise, if no constraints are violated and ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, c contains the row scale factors for $A$, ${D}_{C}$; each element of c is positive.
14:  $\mathbf{b}\left[\mathit{dim}\right]$ComplexInput/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 ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}×{\mathbf{pdb}}\right)$ 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 $n$ by $r$ right-hand side matrix $B$.
On exit: if ${\mathbf{equed}}=\mathrm{Nag_NoEquilibration}$, b is not modified.
If ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ and ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, b is overwritten by ${D}_{R}B$.
If ${\mathbf{trans}}=\mathrm{Nag_Trans}$ or $\mathrm{Nag_ConjTrans}$ and ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, b is overwritten by ${D}_{C}B$.
15:  $\mathbf{pdb}$IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array b.
Constraints:
• if ${\mathbf{order}}=\mathrm{Nag_ColMajor}$, ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• if ${\mathbf{order}}=\mathrm{Nag_RowMajor}$, ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
16:  $\mathbf{x}\left[\mathit{dim}\right]$ComplexOutput
Note: the dimension, dim, of the array x must be at least
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{pdx}}×{\mathbf{nrhs}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• $\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}×{\mathbf{pdx}}\right)$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
The $\left(i,j\right)$th element of the matrix $X$ is stored in
• ${\mathbf{x}}\left[\left(j-1\right)×{\mathbf{pdx}}+i-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_ColMajor}$;
• ${\mathbf{x}}\left[\left(i-1\right)×{\mathbf{pdx}}+j-1\right]$ when ${\mathbf{order}}=\mathrm{Nag_RowMajor}$.
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR or NE_SINGULAR_WP, the $n$ by $r$ solution matrix $X$ to the original system of equations. Note that the arrays $A$ and $B$ are modified on exit if ${\mathbf{equed}}\ne \mathrm{Nag_NoEquilibration}$, and the solution to the equilibrated system is ${D}_{C}^{-1}X$ if ${\mathbf{trans}}=\mathrm{Nag_NoTrans}$ and ${\mathbf{equed}}=\mathrm{Nag_ColumnEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$, or ${D}_{R}^{-1}X$ if ${\mathbf{trans}}=\mathrm{Nag_Trans}$ or $\mathrm{Nag_ConjTrans}$ and ${\mathbf{equed}}=\mathrm{Nag_RowEquilibration}$ or $\mathrm{Nag_RowAndColumnEquilibration}$.
17:  $\mathbf{pdx}$IntegerInput
On entry: the stride separating row or column elements (depending on the value of order) in the array x.
Constraints:
• if ${\mathbf{order}}=\mathrm{Nag_ColMajor}$, ${\mathbf{pdx}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$;
• if ${\mathbf{order}}=\mathrm{Nag_RowMajor}$, ${\mathbf{pdx}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
18:  $\mathbf{rcond}$double *Output
On exit: if no constraints are violated, an estimate of the reciprocal condition number of the matrix $A$ (after equilibration if that is performed), computed as ${\mathbf{rcond}}=1.0/\left({‖A‖}_{1}{‖{A}^{-1}‖}_{1}\right)$.
19:  $\mathbf{ferr}\left[{\mathbf{nrhs}}\right]$doubleOutput
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR or NE_SINGULAR_WP, an estimate of the forward error bound for each computed solution vector, such that ${‖{\stackrel{^}{x}}_{j}-{x}_{j}‖}_{\infty }/{‖{x}_{j}‖}_{\infty }\le {\mathbf{ferr}}\left[j-1\right]$ where ${\stackrel{^}{x}}_{j}$ is the $j$th column of the computed solution returned in the array x and ${x}_{j}$ is the corresponding column of the exact solution $X$. The estimate is as reliable as the estimate for rcond, and is almost always a slight overestimate of the true error.
20:  $\mathbf{berr}\left[{\mathbf{nrhs}}\right]$doubleOutput
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR or NE_SINGULAR_WP, an estimate of the component-wise relative backward error of each computed solution vector ${\stackrel{^}{x}}_{j}$ (i.e., the smallest relative change in any element of $A$ or $B$ that makes ${\stackrel{^}{x}}_{j}$ an exact solution).
21:  $\mathbf{recip_growth_factor}$double *Output
On exit: if ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_NOERROR, the reciprocal pivot growth factor $‖A‖/‖U‖$, where $‖.‖$ denotes the maximum absolute element norm. If ${\mathbf{recip_growth_factor}}\ll 1$, the stability of the $LU$ factorization of (equilibrated) $A$ could be poor. This also means that the solution x, condition estimate rcond, and forward error bound ferr could be unreliable. If the factorization fails with ${\mathbf{fail}}\mathbf{.}\mathbf{code}=$ NE_SINGULAR, then ${\mathbf{recip_growth_factor}}$ contains the reciprocal pivot growth factor for the leading ${\mathbf{fail}}\mathbf{.}\mathbf{errnum}$ columns of $A$.
22:  $\mathbf{fail}$NagError *Input/Output
The NAG error argument (see Section 3.7 in How to Use the NAG Library and its Documentation).

## 6Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 2.3.1.2 in How to Use the NAG Library and its Documentation for further information.
On entry, argument $〈\mathit{\text{value}}〉$ had an illegal value.
NE_INT
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, ${\mathbf{pdaf}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdaf}}>0$.
On entry, ${\mathbf{pdb}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdb}}>0$.
On entry, ${\mathbf{pdx}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdx}}>0$.
NE_INT_2
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, ${\mathbf{pdaf}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdaf}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry, ${\mathbf{pdb}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry, ${\mathbf{pdb}}=〈\mathit{\text{value}}〉$ and ${\mathbf{nrhs}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdb}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
On entry, ${\mathbf{pdx}}=〈\mathit{\text{value}}〉$ and ${\mathbf{n}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdx}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{n}}\right)$.
On entry, ${\mathbf{pdx}}=〈\mathit{\text{value}}〉$ and ${\mathbf{nrhs}}=〈\mathit{\text{value}}〉$.
Constraint: ${\mathbf{pdx}}\ge \mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(1,{\mathbf{nrhs}}\right)$.
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 2.7.6 in How to Use the NAG Library and its Documentation for further information.
NE_NO_LICENCE
Your licence key may have expired or may not have been installed correctly.
See Section 2.7.5 in How to Use the NAG Library and its Documentation for further information.
NE_SINGULAR
Element $〈\mathit{\text{value}}〉$ of the diagonal is exactly zero. The factorization has been completed, but the factor $U$ is exactly singular, so the solution and error bounds could not be computed. ${\mathbf{rcond}}=0.0$ is returned.
NE_SINGULAR_WP
$U$ is nonsingular, but rcond is less than machine precision, meaning that the matrix is singular to working precision. Nevertheless, the solution and error bounds are computed because there are a number of situations where the computed solution can be more accurate than the value of rcond would suggest.

## 7Accuracy

For each right-hand side vector $b$, the computed solution $\stackrel{^}{x}$ is the exact solution of a perturbed system of equations $\left(A+E\right)\stackrel{^}{x}=b$, where
 $E≤cnεPLU ,$
$c\left(n\right)$ is a modest linear function of $n$, and $\epsilon$ is the machine precision. See Section 9.3 of Higham (2002) for further details.
If $x$ is the true solution, then the computed solution $\stackrel{^}{x}$ satisfies a forward error bound of the form
 $x-x^∞ x^∞ ≤ wc condA,x^,b$
where $\mathrm{cond}\left(A,\stackrel{^}{x},b\right)={‖\left|{A}^{-1}\right|\left(\left|A\right|\left|\stackrel{^}{x}\right|+\left|b\right|\right)‖}_{\infty }/{‖\stackrel{^}{x}‖}_{\infty }\le \mathrm{cond}\left(A\right)={‖\left|{A}^{-1}\right|\left|A\right|‖}_{\infty }\le {\kappa }_{\infty }\left(A\right)$. If $\stackrel{^}{x}$ is the $j$th column of $X$, then ${w}_{c}$ is returned in ${\mathbf{berr}}\left[j-1\right]$ and a bound on ${‖x-\stackrel{^}{x}‖}_{\infty }/{‖\stackrel{^}{x}‖}_{\infty }$ is returned in ${\mathbf{ferr}}\left[j-1\right]$. See Section 4.4 of Anderson et al. (1999) for further details.

## 8Parallelism and Performance

nag_zgesvx (f07apc) is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
nag_zgesvx (f07apc) 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.

The factorization of $A$ requires approximately $\frac{8}{3}{n}^{3}$ floating-point operations.
Estimating the forward error involves solving a number of systems of linear equations of the form $Ax=b$ or ${A}^{\mathrm{T}}x=b$; the number is usually $4$ or $5$ and never more than $11$. Each solution involves approximately $8{n}^{2}$ operations.
In practice the condition number estimator is very reliable, but it can underestimate the true condition number; see Section 15.3 of Higham (2002) for further details.
The real analogue of this function is nag_dgesvx (f07abc).

## 10Example

This example solves the equations
 $AX=B ,$
where $A$ is the general matrix
 $A= -1.34+02.55i 0.28+3.17i -6.39-02.20i 0.72-00.92i -1.70-14.10i 33.10-1.50i -1.50+13.40i 12.90+13.80i -3.29-02.39i -1.91+4.42i -0.14-01.35i 1.72+01.35i 2.41+00.39i -0.56+1.47i -0.83-00.69i -1.96+00.67i$
and
 $B= 26.26+51.78i 31.32-06.70i 64.30-86.80i 158.60-14.20i -5.75+25.31i -2.15+30.19i 1.16+02.57i -2.56+07.55i .$
Error estimates for the solutions, information on scaling, an estimate of the reciprocal of the condition number of the scaled matrix $A$ and an estimate of the reciprocal of the pivot growth factor for the factorization of $A$ are also output.

### 10.1Program Text

Program Text (f07apce.c)

### 10.2Program Data

Program Data (f07apce.d)

### 10.3Program Results

Program Results (f07apce.r)

© The Numerical Algorithms Group Ltd, Oxford, UK. 2017