Please note that the recommended version of Scilab is 2023.1.0. This page might be outdated.

See the recommended documentation of this function

# conjgrad

conjugate gradient solvers

### Calling Sequence

[x, flag, err, iter, res] = conjgrad(A, b [, method [, tol [, maxIter [, M [, M2 [, x0 [, verbose]]]]]]]) [x, flag, err, iter, res] = conjgrad(A, b [, method [, key=value,...]])

### Arguments

- A
a matrix, or a function, or a list computing

`A*x`

for each given`x`

. The following is a description of the computation of A*x depending on the type of A.`matrix.`

If A is a matrix, it can be dense or sparse`function.`

If A is a function, it must have the following header :function y=A(x)

`list.`

If A is a list, the first element of the list is expected to be a function and the other elements in the list are the arguments of the function, from index 2 to the end. When the function is called, the current value of x is passed to the function as the first argument. The other arguments passed are the one given in the list.

- b
right hand side vector (size: nx1)

- mehtod
scalar string, "pcg", "cgs", "bicg" or "bicgstab" (default "bicgstab")

- tol
error relative tolerance (default: 1e-8). The termination criteria is based on the 2-norm of the residual r=b-Ax, divided by the 2-norm of the right hand side b.

- maxIter
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))

- verbose
set to 1 to enable verbose logging (default 0)

- x
solution vector

- flag
0 if

`conjgrad`

converged to the desired tolerance within`maxi`

iterations, 1 else- err
final relative norm of the residual (the 2-norm of the right-hand side b is used)

- iter
number of iterations performed

- res
vector of the residual relative 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 `method`

input argument selects the solver to be used:

- "pcg" Preconditioned Conjugate Gradient
- "cgs" preconditioned Conjugate Gradient Squared
- "bicg" preconditioned BiConjugate Gradient
- "bicgstab" preconditioned BiConjugate Gradient Stabilized (default)

If `method="pcg"`

, then 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`

.
Otherwise (`method="cgs", "bicg" or "bicgstab"`

),
`A`

just needs to be square.

### Example with well-conditionned and ill-conditionned problems

In the following example, two linear systems are solved. The first maxtrix has a condition number equals to ~0.02, which makes the algorithm converge in exactly 10 iterations. Since this is the size of the matrix, it is an expected behaviour for a gradient conjugate method. The second one has a low condition number equals to 1.d-6, which makes the algorithm converge in a larger 22 iterations. This is why the parameter maxIter is set to 30. See below for other examples of the "key=value" syntax.

// 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] = conjgrad(A, b, "bicg", 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] = conjgrad(A, b, method="pcg", maxIter=30, tol=1d-12); mprintf(" fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

### Examples with A given as a sparse matrix, or function, or list

The following example shows that the method can handle sparse matrices as well. It also shows the case where a function, computing the right-hand side, is given to the "conjgrad" primitive. The final case shown by this example, is when a list is passed to the primitive.

// 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); // Convert A into a sparse matrix Asparse=sparse(A); [x, fail, err, iter, res] = conjgrad(Asparse, b, "cgs", maxIter=30, tol=1d-12); mprintf(" fail=%d, iter=%d, errrel=%e\n",fail,iter,err) // Define a function which computes the right-hand side. function y=Atimesx(x) 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]; y = A*x endfunction // Pass the script Atimesx to the primitive [x, fail, err, iter, res] = conjgrad(Atimesx, b, maxIter=30, tol=1d-12); mprintf(" fail=%d, iter=%d, errrel=%e\n",fail,iter,err) // Define a function which computes the right-hand side. function y=Atimesxbis(x, A) y = A*x endfunction // Pass a list to the primitive Alist = list(Atimesxbis, Asparse); [x, fail, err, iter, res] = conjgrad(Alist, b, maxIter=30, tol=1d-12); mprintf(" fail=%d, iter=%d, errrel=%e\n",fail,iter,err)

### Examples with key=value syntax

The following example shows how to pass arguments with the "key=value" syntax. This allows to set non-positionnal arguments, that is, to set arguments which are not depending on their order in the list of arguments. The available keys are the names of the optional arguments, that is : tol, maxIter, %M, %M2, x0, verbose. Notice that, in the following example, the verbose option is given before the maxIter option. Without the "key=value" syntax, the positionnal arguments would require that maxIter come first and verbose after.

// Example of an argument passed with key=value syntax A = [100 1; 1 10]; b = [101; 11]; [xcomputed, flag, err, iter, res] = conjgrad(A, b, verbose=1); // With key=value syntax, the order does not matter [xcomputed, flag, err, iter, res] = conjgrad(A, b, verbose=1, maxIter=0);

### See Also

### References

**PCG**

"Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods", Barrett, Berry, Chan, Demmel, Donato, Dongarra, Eijkhout, Pozo, Romine, and Van der Vorst, SIAM Publications, 1993, ftp netlib2.cs.utk.edu/linalg/templates.ps

"Iterative Methods for Sparse Linear Systems, Second Edition", Saad, SIAM Publications, 2003, ftp ftp.cs.umn.edu/dept/users/saad/PS/all_ps.zip

**CGS**

"CGS, A Fast Lanczos-Type Solver for Nonsymmetric Linear systems" by Peter Sonneveld.

**BICG**

"Numerical Recipes: The Art of Scientific Computing." (third ed.) by William Press, Saul Teukolsky, William Vetterling, Brian Flannery.

http://apps.nrbook.com/empanel/index.html?pg=87

**BICGSTAB**

"Bi-CGSTAB: A Fast and Smoothly Converging Variant of Bi-CG for the Solution of Nonsymmetric Linear Systems" by Henk van der Vorst. 339

### History

Версия | Описание |

5.5.0 | Introduction |

Report an issue | ||

<< Linear Equations (Iterative Solvers) | Linear Equations (Iterative Solvers) | gmres >> |