G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

# NAG Library Routine DocumentG02ABF

Note:  before using this routine, please read the Users' Note for your implementation to check the interpretation of bold italicised terms and other implementation-dependent details.

## 1  Purpose

G02ABF computes the nearest correlation matrix, in the Frobenius norm or weighted Frobenius norm, and optionally with bounds on the eigenvalues, to a given square, input matrix.

## 2  Specification

 SUBROUTINE G02ABF ( G, LDG, N, OPT, ALPHA, W, ERRTOL, MAXITS, MAXIT, X, LDX, ITER, FEVAL, NRMGRD, IFAIL)
 INTEGER LDG, N, MAXITS, MAXIT, LDX, ITER, FEVAL, IFAIL REAL (KIND=nag_wp) G(LDG,N), ALPHA, W(N), ERRTOL, X(LDX,N), NRMGRD CHARACTER(1) OPT

## 3  Description

Finds the nearest correlation matrix $X$ by minimizing $\frac{1}{2}{‖G-X‖}^{2}$ where $G$ is an approximate correlation matrix.
The norm can either be the Frobenius norm or the weighted Frobenius norm $\frac{1}{2}{‖{W}^{\frac{1}{2}}\left(G-X\right){W}^{\frac{1}{2}}‖}_{F}^{2}$.
You can optionally specify a lower bound on the eigenvalues, $\alpha$, of the computed correlation matrix, forcing the matrix to be positive definite, $0<\alpha <1$.
Note that if the weights vary by several orders of magnitude from one another the algorithm may fail to converge.

## 4  References

Borsdorf R and Higham N J (2010) A preconditioned (Newton) algorithm for the nearest correlation matrix IMA Journal of Numerical Analysis 30(1) 94–107
Qi H and Sun D (2006) A quadratically convergent Newton method for computing the nearest correlation matrix SIAM J. Matrix AnalAppl 29(2) 360–385

## 5  Parameters

1:     G(LDG,N) – REAL (KIND=nag_wp) arrayInput/Output
On entry: $G$, the initial matrix.
On exit: a symmetric matrix $\frac{1}{2}\left(G+{G}^{\mathrm{T}}\right)$ with the diagonal set to $I$.
2:     LDG – INTEGERInput
On entry: the first dimension of the array G as declared in the (sub)program from which G02ABF is called.
Constraint: ${\mathbf{LDG}}\ge {\mathbf{N}}$.
3:     N – INTEGERInput
On entry: the order of the matrix $G$.
Constraint: ${\mathbf{N}}>0$.
4:     OPT – CHARACTER(1)Input
On entry: indicates the problem to be solved.
${\mathbf{OPT}}=\text{'A'}$
The lower bound problem is solved.
${\mathbf{OPT}}=\text{'W'}$
The weighted norm problem is solved.
${\mathbf{OPT}}=\text{'B'}$
Both problems are solved.
Constraint: ${\mathbf{OPT}}=\text{'A'}$, $\text{'W'}$ or $\text{'B'}$.
5:     ALPHA – REAL (KIND=nag_wp)Input
On entry: the value of $\alpha$.
If ${\mathbf{OPT}}=\text{'W'}$, ALPHA need not be set.
Constraint: $0.0<{\mathbf{ALPHA}}<1.0$.
6:     W(N) – REAL (KIND=nag_wp) arrayInput/Output
On entry: the square roots of the diagonal elements of $W$, that is the diagonal of ${W}^{\frac{1}{2}}$.
If ${\mathbf{OPT}}=\text{'A'}$, W need not be set.
On exit: if ${\mathbf{OPT}}=\text{'W'}$ or $\text{'B'}$, the array is scaled so $0<{\mathbf{W}}\left(\mathit{i}\right)\le 1$, for $\mathit{i}=1,2,\dots ,n$.
Constraint: ${\mathbf{W}}\left(\mathit{i}\right)>0.0$, for $\mathit{i}=1,2,\dots ,n$.
7:     ERRTOL – REAL (KIND=nag_wp)Input
On entry: the termination tolerance for the Newton iteration. If ${\mathbf{ERRTOL}}\le 0.0$ then  is used.
8:     MAXITS – INTEGERInput
On entry: specifies the maximum number of iterations to be used by the iterative scheme to solve the linear algebraic equations at each Newton step.
If ${\mathbf{MAXITS}}\le 0$, $2×{\mathbf{N}}$ is used.
9:     MAXIT – INTEGERInput
On entry: specifies the maximum number of Newton iterations.
If ${\mathbf{MAXIT}}\le 0$, $200$ is used.
10:   X(LDX,N) – REAL (KIND=nag_wp) arrayOutput
On exit: contains the nearest correlation matrix.
11:   LDX – INTEGERInput
On entry: the first dimension of the array X as declared in the (sub)program from which G02ABF is called.
Constraint: ${\mathbf{LDX}}\ge {\mathbf{N}}$.
12:   ITER – INTEGEROutput
On exit: the number of Newton steps taken.
13:   FEVAL – INTEGEROutput
On exit: the number of function evaluations of the dual problem.
14:   NRMGRD – REAL (KIND=nag_wp)Output
On exit: the norm of the gradient of the last Newton step.
15:   IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to $0$, $-1\text{​ or ​}1$. If you are unfamiliar with this parameter you should refer to Section 3.3 in the Essential Introduction for details.
For environments where it might be inappropriate to halt program execution when an error is detected, the value $-1\text{​ or ​}1$ is recommended. If the output of error messages is undesirable, then the value $1$ is recommended. Otherwise, if you are not familiar with this parameter, the recommended value is $0$. When the value $-\mathbf{1}\text{​ or ​}\mathbf{1}$ is used it is essential to test the value of IFAIL on exit.
On exit: ${\mathbf{IFAIL}}={\mathbf{0}}$ unless the routine detects an error or a warning has been flagged (see Section 6).

## 6  Error Indicators and Warnings

If on entry ${\mathbf{IFAIL}}={\mathbf{0}}$ or $-{\mathbf{1}}$, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
${\mathbf{IFAIL}}=1$
 On entry, ${\mathbf{N}}\le 0$, or ${\mathbf{LDG}}<{\mathbf{N}}$, or ${\mathbf{LDX}}<{\mathbf{N}}$. or ${\mathbf{OPT}}\ne \text{'A'}$, $\text{'W'}$ or $\text{'B'}$. or ${\mathbf{ALPHA}}\le 0.0$ or ${\mathbf{ALPHA}}\ge 1.0$. or ${\mathbf{W}}\left(i\right)\le 0$ for at least one $i=1,2,\dots ,n$.
${\mathbf{IFAIL}}=2$
The routine fails to converge in MAXIT iterations. Increase MAXIT or check the call to the routine.
${\mathbf{IFAIL}}=3$
machine precision is limiting convergence. In this instance the returned value of X may be useful.
${\mathbf{IFAIL}}=4$
${\mathbf{IFAIL}}=-999$
Internal memory allocation failed.

## 7  Accuracy

The returned accuracy is controlled by ERRTOL and limited by machine precision.

Arrays are internally allocated by G02ABF. The total size of these arrays is $12×{\mathbf{N}}+3×{\mathbf{N}}×{\mathbf{N}}+\mathrm{max}\phantom{\rule{0.125em}{0ex}}\left(2×{\mathbf{N}}×{\mathbf{N}}+6×{\mathbf{N}}+1,120+9×{\mathbf{N}}\right)$ real elements and $5×{\mathbf{N}}+3$ integer elements. All allocated memory is freed before return of G02ABF.

## 9  Example

This example finds the nearest correlation matrix to:
 $G = 2 -1 0 0 -1 2 -1 0 0 -1 2 -1 0 0 -1 2$
weighted by ${W}^{\frac{1}{2}}=\mathrm{diag}\left(100,20,20,20\right)$ with minimum eigenvalue $0.02$.

### 9.1  Program Text

Program Text (g02abfe.f90)

### 9.2  Program Data

Program Data (g02abfe.d)

### 9.3  Program Results

Program Results (g02abfe.r)