G02AJF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

NAG Library Routine Document


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
    7  Accuracy

1  Purpose

G02AJF computes the nearest correlation matrix, using element-wise weighting in the Frobenius norm and optionally with bounds on the eigenvalues, to a given square, input matrix.

2  Specification


3  Description

G02AJF finds the nearest correlation matrix, X, to an approximate correlation matrix, G, using element-wise weighting, this minimizes H G-X F , where C=AB denotes the matrix C with elements Cij=Aij×Bij.
You can optionally specify a lower bound on the eigenvalues, α, of the computed correlation matrix, forcing the matrix to be strictly positive definite, if 0<α<1.
Zero elements in H should be used when you wish to put no emphasis on the corresponding element of G. The algorithm scales H so that the maximum element is 1. It is this scaled matrix that is used in computing the norm above and for the stopping criteria described in Section 7.
Note that if the elements in H 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
Jiang K, Sun D and Toh K-C (To appear) An inexact accelerated proximal gradient method for large scale linearly constrained convex SDP
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:     GLDGN – REAL (KIND=nag_wp) arrayInput/Output
On entry: G, the initial matrix.
On exit: G is overwritten.
2:     LDG – INTEGERInput
On entry: the first dimension of the array G as declared in the (sub)program from which G02AJF is called.
Constraint: LDGN.
3:     N – INTEGERInput
On entry: the order of the matrix G.
Constraint: N>0.
4:     ALPHA – REAL (KIND=nag_wp)Input
On entry: the value of α.
If ALPHA<0.0, 0.0 is used.
Constraint: ALPHA<1.0.
5:     HLDHN – REAL (KIND=nag_wp) arrayInput/Output
On entry: the matrix of weights H.
On exit: a symmetric matrix 12 H+HT  with its diagonal elements set to zero and the remaining elements scaled so that the maximum element is 1.0.
Constraint: Hij0.0, for all i and j=1,2,,n, ij.
6:     LDH – INTEGERInput
On entry: the first dimension of the array H as declared in the (sub)program from which G02AJF is called.
Constraint: LDHN.
7:     ERRTOL – REAL (KIND=nag_wp)Input
On entry: the termination tolerance for the iteration. If ERRTOL0.0 then N×machine precision is used. See Section 7 for further details.
8:     MAXIT – INTEGERInput
On entry: specifies the maximum number of iterations to be used.
If MAXIT0, 200 is used.
9:     XLDXN – REAL (KIND=nag_wp) arrayOutput
On exit: contains the nearest correlation matrix.
10:   LDX – INTEGERInput
On entry: the first dimension of the array X as declared in the (sub)program from which G02AJF is called.
Constraint: LDXN.
11:   ITER – INTEGEROutput
On exit: the number of iterations taken.
12:   NORM – REAL (KIND=nag_wp)Output
On exit: the value of HG-XF after the final iteration.
13:   IFAIL – INTEGERInput/Output
On entry: IFAIL must be set to 0, -1​ 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​ 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 -1​ or ​1 is used it is essential to test the value of IFAIL on exit.
On exit: IFAIL=0 unless the routine detects an error or a warning has been flagged (see Section 6).

6  Error Indicators and Warnings

If on entry IFAIL=0 or -1, explanatory error messages are output on the current error message unit (as defined by X04AAF).
Errors or warnings detected by the routine:
On entry, N=value.
Constraint: N>0.
On entry, LDG=value and N=value.
Constraint: LDGN.
On entry, LDH=value and N=value.
Constraint: LDHN.
On entry, LDX=value and N=value.
Constraint: LDXN.
On entry, ALPHA=value.
Constraint: ALPHA<1.0.
On entry, one or more of the off-diagonal elements of H were negative.
Routine fails to converge in value iterations.
Increase MAXIT or check the call to the routine.
Failure to solve intermediate eigenproblem. This should not occur. Please contact NAG with details of your call.
An unexpected error has been triggered by this routine. Please contact NAG.
See Section 3.8 in the Essential Introduction for further information.
Your licence key may have expired or may not have been installed correctly.
See Section 3.7 in the Essential Introduction for further information.
Dynamic memory allocation failed.
See Section 3.6 in the Essential Introduction for further information.

7  Accuracy

The returned accuracy is controlled by ERRTOL and limited by machine precision. If ei is the value of NORM at the ith iteration, that is
ei = HG-XF ,  
where H has been scaled as described above, then the algorithm terminates when:
ei-ei-1 1+ maxei,ei-1 ERRTOL .  

8  Parallelism and Performance

G02AJF is threaded by NAG for parallel execution in multithreaded implementations of the NAG Library.
G02AJF 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 routine. Please also consult the Users' Note for your implementation for any additional implementation-specific information.

9  Further Comments

Arrays are internally allocated by G02AJF. The total size of these arrays is 15×N+5×N×N+max2×N×N+6×N+1,120+9×N real elements and 5×N+3 integer elements. All allocated memory is freed before return of G02AJF.

10  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:
H = 0.0 10.0 0.0 0.0 10.0 0.0 1.5 1.5 0.0 1.5 0.0 0.0 0.0 1.5 0.0 0.0  
with minimum eigenvalue 0.04.

10.1  Program Text

Program Text (g02ajfe.f90)

10.2  Program Data

Program Data (g02ajfe.d)

10.3  Program Results

Program Results (g02ajfe.r)

G02AJF (PDF version)
G02 Chapter Contents
G02 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2015