pcg

Scilab Function

Last update : 25/04/2007

pcg - precondioned conjugate gradient

Calling Sequence

[x, flag, err, iter, res] = pcg(A, b [, tol [, maxit [, M [, M2 [, x]]]]])

[x, flag, err, iter, res] = pcg(A, b, key=value,...)

Parameters

A: symmetric positive definite full or sparse matrix of size nxn or a function returning A*x for each given x
b: right hand side vector (size: nx1)
tol: error tolerance (default: 1000*%eps)
maxi: maximum number of iterations (default: n)
M: preconditioner: full or sparse matrix or function returning M\x (default: none)
M2: preconditioner: full or sparse matrix or function returning M2\x for each x (default: none)
x0: initial guess vector (default: zeros(n,1))
x: solution vector
flag: 0 if pcg converged to the desired tolerance within maxi iterations, 1 else
err: final relative norm of the residual
iter: number of iterations performed
res: vector of the residual norms

Description

Solves the linear system Ax=b using the conjugate gradient method with or without preconditioning. The preconditionning should be defined by a symmetric positive definite matrix M, or two matrices M1 and M2 such that M=M1*M2. in the case the function solves inv(M)*A*x = inv(M)*b for x. M, M1 and M2 can be Scilab functions with calling sequence y=Milx(x) which computes the corresponding left division y=Mi\x. The A matrix must be a symmetric positive definite matrix (full or sparse) or a function with calling sequence y=Ax(x) which computes y=A*x

Examples


 //Well conditionned problem
 A=[ 94  0   0   0    0   28  0   0   32  0  
     0   59  13  5    0   0   0   10  0   0  
     0   13  72  34   2   0   0   0   0   65 
     0   5   34  114  0   0   0   0   0   55 
     0   0   2   0    70  0   28  32  12  0  
     28  0   0   0    0   87  20  0   33  0  
     0   0   0   0    28  20  71  39  0   0  
     0   10  0   0    32  0   39  46  8   0  
     32  0   0   0    12  33  0   8   82  11 
     0   0   65  55   0   0   0   0   11  100];

  b=ones(10,1);
  [x, fail, err, iter, res]=pcg(A,b,1d-12,15);
  mprintf("      fail=%d, iter=%d, errrel=%e\n",fail,iter,err)


  //Ill contionned one
 A=[ 894     0   0     0   0   28  0   0   1000  70000
      0      5   13    5   0   0   0   0   0     0    
      0      13  72    34  0   0   0   0   0     6500 
      0      5   34    1   0   0   0   0   0     55   
      0      0   0     0   70  0   28  32  12    0    
      28     0   0     0   0   87  20  0   33    0    
      0      0   0     0   28  20  71  39  0     0    
      0      0   0     0   32  0   39  46  8     0    
      1000   0   0     0   12  33  0   8   82    11   
      70000  0   6500  55  0   0   0   0   11    100];
 
  [x, fail, err, iter, res]=pcg(A,b,maxIter=30,tol=1d-12);
  mprintf("      fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

   //sparse matrix
   A=sparse(A);
   [x, fail, err, iter, res]=pcg(A,b,maxIter=30,tol=1d-12);
   mprintf("      fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

   //using a function
   function y=Atimesx(x,A),y=A*x, endfunction

   //A passed by the calling context
   [x, fail, err, iter, res]=pcg(Atimesx,b,maxIter=30,tol=1d-12);
   mprintf("      fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

   // A passed as an argument
   [x, fail, err, iter, res]=pcg(list(Atimesx,A),b,maxIter=30,tol=1d-12);
   mprintf("      fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

Author

Sage Group (IRISA, 2004)

Calling Sequence

Parameters

Description

Examples

See Also

Author