# NAG CL Interfacef11jqc (complex_​herm_​solve_​ilu)

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## 1Purpose

f11jqc solves a complex sparse Hermitian system of linear equations, represented in symmetric coordinate storage format, using a conjugate gradient or Lanczos method, with incomplete Cholesky preconditioning.

## 2Specification

 #include
 void f11jqc (Nag_SparseSym_Method method, Integer n, Integer nnz, const Complex a[], Integer la, const Integer irow[], const Integer icol[], const Integer ipiv[], const Integer istr[], const Complex b[], double tol, Integer maxitn, Complex x[], double *rnorm, Integer *itn, NagError *fail)
The function may be called by the names: f11jqc, nag_sparse_complex_herm_solve_ilu or nag_sparse_herm_chol_sol.

## 3Description

f11jqc solves a complex sparse Hermitian linear system of equations
 $Ax=b,$
using a preconditioned conjugate gradient method (see Meijerink and Van der Vorst (1977)), or a preconditioned Lanczos method based on the algorithm SYMMLQ (see Paige and Saunders (1975)). The conjugate gradient method is more efficient if $A$ is positive definite, but may fail to converge for indefinite matrices. In this case the Lanczos method should be used instead. For further details see Barrett et al. (1994).
f11jqc uses the incomplete Cholesky factorization determined by f11jnc as the preconditioning matrix. A call to f11jqc must always be preceded by a call to f11jnc. Alternative preconditioners for the same storage scheme are available by calling f11jsc.
The matrix $A$ and the preconditioning matrix $M$ are represented in symmetric coordinate storage (SCS) format (see Section 2.1.2 in the F11 Chapter Introduction) in the arrays a, irow and icol, as returned from f11jnc. The array a holds the nonzero entries in the lower triangular parts of these matrices, while irow and icol hold the corresponding row and column indices.

## 4References

Barrett R, Berry M, Chan T F, Demmel J, Donato J, Dongarra J, Eijkhout V, Pozo R, Romine C and Van der Vorst H (1994) Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods SIAM, Philadelphia
Meijerink J and Van der Vorst H (1977) An iterative solution method for linear systems of which the coefficient matrix is a symmetric M-matrix Math. Comput. 31 148–162
Paige C C and Saunders M A (1975) Solution of sparse indefinite systems of linear equations SIAM J. Numer. Anal. 12 617–629

## 5Arguments

1: $\mathbf{method}$Nag_SparseSym_Method Input
On entry: specifies the iterative method to be used.
${\mathbf{method}}=\mathrm{Nag_SparseSym_CG}$
${\mathbf{method}}=\mathrm{Nag_SparseSym_SYMMLQ}$
Lanczos method (SYMMLQ).
Constraint: ${\mathbf{method}}=\mathrm{Nag_SparseSym_CG}$ or $\mathrm{Nag_SparseSym_SYMMLQ}$.
2: $\mathbf{n}$Integer Input
On entry: $n$, the order of the matrix $A$. This must be the same value as was supplied in the preceding call to f11jnc.
Constraint: ${\mathbf{n}}\ge 1$.
3: $\mathbf{nnz}$Integer Input
On entry: the number of nonzero elements in the lower triangular part of the matrix $A$. This must be the same value as was supplied in the preceding call to f11jnc.
Constraint: $1\le {\mathbf{nnz}}\le {\mathbf{n}}×\left({\mathbf{n}}+1\right)/2$.
4: $\mathbf{a}\left[{\mathbf{la}}\right]$const Complex Input
On entry: the values returned in the array a by a previous call to f11jnc.
5: $\mathbf{la}$Integer Input
On entry: the dimension of the arrays a, irow and icol. This must be the same value as was supplied in the preceding call to f11jnc.
Constraint: ${\mathbf{la}}\ge 2×{\mathbf{nnz}}$.
6: $\mathbf{irow}\left[{\mathbf{la}}\right]$const Integer Input
7: $\mathbf{icol}\left[{\mathbf{la}}\right]$const Integer Input
8: $\mathbf{ipiv}\left[{\mathbf{n}}\right]$const Integer Input
9: $\mathbf{istr}\left[{\mathbf{n}}+1\right]$const Integer Input
On entry: the values returned in arrays irow, icol, ipiv and istr by a previous call to f11jnc.
10: $\mathbf{b}\left[{\mathbf{n}}\right]$const Complex Input
On entry: the right-hand side vector $b$.
11: $\mathbf{tol}$double Input
On entry: the required tolerance. Let ${x}_{k}$ denote the approximate solution at iteration $k$, and ${r}_{k}$ the corresponding residual. The algorithm is considered to have converged at iteration $k$ if
 $‖rk‖∞≤τ×(‖b‖∞+‖A‖∞‖xk‖∞).$
If ${\mathbf{tol}}\le 0.0$, $\tau =\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(\sqrt{\epsilon },10\epsilon ,\sqrt{n}\epsilon \right)$ is used, where $\epsilon$ is the machine precision. Otherwise $\tau =\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left({\mathbf{tol}},10\epsilon ,\sqrt{n}\epsilon \right)$ is used.
Constraint: ${\mathbf{tol}}<1.0$.
12: $\mathbf{maxitn}$Integer Input
On entry: the maximum number of iterations allowed.
Constraint: ${\mathbf{maxitn}}\ge 1$.
13: $\mathbf{x}\left[{\mathbf{n}}\right]$Complex Input/Output
On entry: an initial approximation to the solution vector $x$.
On exit: an improved approximation to the solution vector $x$.
14: $\mathbf{rnorm}$double * Output
On exit: the final value of the residual norm ${‖{r}_{k}‖}_{\infty }$, where $k$ is the output value of itn.
15: $\mathbf{itn}$Integer * Output
On exit: the number of iterations carried out.
16: $\mathbf{fail}$NagError * Input/Output
The NAG error argument (see Section 7 in the Introduction to the NAG Library CL Interface).

## 6Error Indicators and Warnings

Check that the call to f11jqc has been preceded by a valid call to f11jnc, and that the arrays a, irow, and icol have not been corrupted between the two calls.
Check that a, irow, icol, ipiv and istr have not been corrupted between calls to f11jnc.
NE_ACCURACY
The required accuracy could not be obtained. However a reasonable accuracy has been achieved.
NE_ALLOC_FAIL
Dynamic memory allocation failed.
See Section 3.1.2 in the Introduction to the NAG Library CL Interface for further information.
On entry, argument $⟨\mathit{\text{value}}⟩$ had an illegal value.
NE_COEFF_NOT_POS_DEF
The matrix of the coefficients a appears not to be positive definite. The computation cannot continue.
NE_CONVERGENCE
The solution has not converged after $⟨\mathit{\text{value}}⟩$ iterations.
NE_INT
On entry, ${\mathbf{maxitn}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{maxitn}}\ge 1$.
On entry, ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{n}}\ge 1$.
On entry, ${\mathbf{nnz}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{nnz}}\ge 1$.
NE_INT_2
On entry, ${\mathbf{la}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{nnz}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{la}}\ge 2×{\mathbf{nnz}}$.
On entry, ${\mathbf{nnz}}=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{nnz}}\le {\mathbf{n}}×\left({\mathbf{n}}+1\right)/2$.
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.
A serious error, code $⟨\mathit{\text{value}}⟩$, has occurred in an internal call. Check all function calls and array sizes. Seek expert help.
NE_INVALID_SCS
On entry, $i=⟨\mathit{\text{value}}⟩$, ${\mathbf{icol}}\left[i-1\right]=⟨\mathit{\text{value}}⟩$, ${\mathbf{irow}}\left[i-1\right]=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{icol}}\left[i-1\right]\ge 1$ and ${\mathbf{icol}}\left[i-1\right]\le {\mathbf{irow}}\left[i-1\right]$.
On entry, $i=⟨\mathit{\text{value}}⟩$, ${\mathbf{irow}}\left[i-1\right]=⟨\mathit{\text{value}}⟩$ and ${\mathbf{n}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{irow}}\left[i-1\right]\ge 1$ and ${\mathbf{irow}}\left[i-1\right]\le {\mathbf{n}}$.
NE_INVALID_SCS_PRECOND
On entry, istr appears to be invalid.
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.
NE_NOT_STRICTLY_INCREASING
On entry, ${\mathbf{a}}\left[i-1\right]$ is out of order: $i=⟨\mathit{\text{value}}⟩$.
On entry, the location (${\mathbf{irow}}\left[i-1\right],{\mathbf{icol}}\left[i-1\right]$) is a duplicate: $i=⟨\mathit{\text{value}}⟩$.
NE_PRECOND_NOT_POS_DEF
The preconditioner appears not to be positive definite. The computation cannot continue.
NE_REAL
On entry, ${\mathbf{tol}}=⟨\mathit{\text{value}}⟩$.
Constraint: ${\mathbf{tol}}<1.0$.

## 7Accuracy

On successful termination, the final residual ${r}_{k}=b-A{x}_{k}$, where $k={\mathbf{itn}}$, satisfies the termination criterion
 $‖rk‖∞≤τ×(‖b‖∞+‖A‖∞‖xk‖∞).$
The value of the final residual norm is returned in rnorm.

## 8Parallelism and Performance

Background information to multithreading can be found in the Multithreading documentation.
f11jqc is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
f11jqc 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 time taken by f11jqc for each iteration is roughly proportional to the value of nnzc returned from the preceding call to f11jnc. One iteration with the Lanczos method (SYMMLQ) requires a slightly larger number of operations than one iteration with the conjugate gradient method.
The number of iterations required to achieve a prescribed accuracy cannot easily be determined a priori, as it can depend dramatically on the conditioning and spectrum of the preconditioned matrix of the coefficients $\overline{A}={M}^{-1}A$.

## 10Example

This example solves a complex sparse Hermitian positive definite system of equations using the conjugate gradient method, with incomplete Cholesky preconditioning.

### 10.1Program Text

Program Text (f11jqce.c)

### 10.2Program Data

Program Data (f11jqce.d)

### 10.3Program Results

Program Results (f11jqce.r)