dlangs_dist.c File Reference

Returns the value of the one norm, the infinity norm, or the element of largest value Modified from SuperLU routine DLANGS. More...

#include <math.h>
#include "superlu_ddefs.h"

Include dependency graph for dlangs_dist.c:

Functions
double	dlangs_dist (char *norm, SuperMatrix *A)

Detailed Description

Returns the value of the one norm, the infinity norm, or the element of largest value Modified from SuperLU routine DLANGS.

Copyright (c) 2003, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from U.S. Dept. of Energy)

All rights reserved.

The source code is distributed under BSD license, see the file License.txt at the top-level directory.

-- SuperLU routine (version 2.0) --
Univ. of California Berkeley, Xerox Palo Alto Research Center,
and Lawrence Berkeley National Lab.
November 15, 1997

Function Documentation

dlangs_dist()

double dlangs_dist	(	char *	norm,
		SuperMatrix *	A
	)

Purpose   
  =======   

  DLANGS_DIST returns the value of the one norm, or the Frobenius norm, or 
  the infinity norm, or the element of largest absolute value of a 
  real matrix A.   

  Description   
  ===========   

  DLANGE returns the value   

     DLANGE = ( max(abs(A(i,j))), NORM = 'M' or 'm'   
              (   
              ( norm1(A),         NORM = '1', 'O' or 'o'   
              (   
              ( normI(A),         NORM = 'I' or 'i'   
              (   
              ( normF(A),         NORM = 'F', 'f', 'E' or 'e'   

  where  norm1  denotes the  one norm of a matrix (maximum column sum), 
  normI  denotes the  infinity norm  of a matrix  (maximum row sum) and 
  normF  denotes the  Frobenius norm of a matrix (square root of sum of 
  squares).  Note that  max(abs(A(i,j)))  is not a  matrix norm.   

  Arguments   
  =========   

  NORM    (input) CHARACTER*1   
          Specifies the value to be returned in DLANGE as described above.   
  A       (input) SuperMatrix*
          The M by N sparse matrix A. 

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