The LA_CHOLMPROVE function uses Cholesky factorization to improve the solution to a system of linear equations, *AX* = *B* (where *A* is symmetric or Hermitian), and provides optional error bounds and backward error estimates.

The LA_CHOLMPROVE function may also be used to improve the solutions for multiple systems of linear equations, with each column of *B* representing a different set of equations. In this case, the result is a* k*-by-*n* array where each of the *k* columns represents the improved solution vector for that set of equations.

LA_CHOLMPROVE is based on the following LAPACK routines:

Output Type |
LAPACK Routine |

Float |
sporfs |

Double |
dporfs |

Complex |
cporfs |

Double complex |
zporfs |

## Examples

The following example program computes an improved solution to a set of 10 equations:

`; Create a symmetric positive-definite array.`

`n = 10`

`seed = 12321`

`a = RANDOMU(seed, n, n, /DOUBLE)`

`a = a ## TRANSPOSE(a)`

`; Create the right-hand side vector b:`

`b = RANDOMU(seed, n, /DOUBLE)`

`; Compute the Cholesky decomposition.`

`achol = a ; make a copy`

`LA_CHOLDC, achol`

`; Compute the first approximation to the solution:`

`x = LA_CHOLSOL(achol, b)`

`; Improve the solution and print the error estimate:`

`xmprove = LA_CHOLMPROVE(a, achol, b, x, $`

FORWARD_ERROR = fError)

PRINT, 'LA_CHOLMPROVE error:', $

MAX(ABS(a ## xmprove - b))

PRINT, 'LA_CHOLMPROVE Error Estimate:', fError

When this program is compiled and run, IDL prints:

LA_CHOLMPROVE error: 3.2529535e-014

LA_CHOLMPROVE Error Estimate: 2.0425691e-012

## Syntax

*Result* = LA_CHOLMPROVE( *Array*, *Achol*, *B*, *X* [, BACKWARD_ERROR=*variable*] [, /DOUBLE] [, FORWARD_ERROR=*variable*] [, /UPPER] )

## Return Value

The result is an *n*-element vector or *k*-by-*n* array.

## Arguments

### Array

The original *n*-by-*n* array of the linear system *AX* = *B*.

### Achol

The* n*-by-*n* Cholesky factorization of *Array*, created by the LA_CHOLDC procedure.

### B

An *n*-element input vector containing the right-hand side of the linear system, or a *k*-by-*n* array, where each of the *k* columns represents a different linear system.

### X

An *n*-element input vector, or a *k*-by-*n* array, containing the approximate solutions to the linear system, created by the LA_CHOLSOL function.

## Keywords

### BACKWARD_ERROR

Set this keyword to a named variable that will contain the relative backward error estimate for each linear system. If *B *is a vector containing a single linear system, then BACKWARD_ERROR will be a scalar. If *B *is an array containing* k* linear systems, then BACKWARD_ERROR will be a* k*-element vector. The backward error is the smallest relative change in any element of *A* or *B* that makes* X* an exact solution.

### DOUBLE

Set this keyword to use double-precision for computations and to return a double-precision (real or complex) result. Set DOUBLE = 0 to use single-precision for computations and to return a single-precision (real or complex) result. The default is /DOUBLE if *Array *is double precision, otherwise the default is DOUBLE = 0.

### FORWARD_ERROR

Set this keyword to a named variable that will contain the estimated forward error bound for each linear system. If *B* is a vector containing a single linear system, then FORWARD_ERROR will be a scalar. If *B* is an array containing *k* linear systems, then FORWARD_ERROR will be a* k*-element vector. For each linear system, if *Xtrue* is the true solution corresponding to *X*, then the forward error is an estimated upper bound for the magnitude of the largest element in (*X* -* Xtrue*) divided by the magnitude of the largest element in *X*.

### UPPER

Set this keyword if *A* contains the upper triangular array, rather than the lower triangular array.

**Note: **If the UPPER keyword is set in LA_CHOLDC and LA_CHOLSOL then the UPPER keyword must also be set in LA_CHOLMPROVE.

## Version History

5.6 |
Introduced |

## Resources and References

For more details, see Anderson et al., *LAPACK Users' Guide*, 3rd ed., SIAM, 1999.

## See Also

## Notes

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