MATLAB Function Reference
minres

Minimum Residual method

Syntax

• ```x = minres(A,b)
minres(A,b,tol)
minres(A,b,tol,maxit)
minres(A,b,tol.maxit,M)
minres(A,b,tol,maxit,M1,M2)
minres(A,b,tol,maxit,M1,M2,x0)
minres(afun,b,tol,maxit,mifun,m2fun,x0,p1,p2,...)
[x,flag] = minres(A,b,...)
[x,flag,relres] = minres(A,b,...)
[x,flag,relres,iter] = minres(A,b,...)
[x,flag,relres,iter,resvec] = minres(A,b,...)
[x,flag,relres,iter,resvec,resveccg] = minres(A,b,...)
```

Description

```x = minres(A,b) ``` attempts to find a minimum norm residual solution `x` to the system of linear equations `A*x=b`. The `n`-by-`n` coefficient matrix `A` must be symmetric but need not be positive definite. It should be large and sparse. The column vector `b` must have length `n`. `A` can be a function `afun` such that `afun(x)` returns `A*x`.

If `minres` converges, a message to that effect is displayed. If `minres` fails to converge after the maximum number of iterations or halts for any reason, a warning message is printed displaying the relative residual `norm(b-A*x)/norm(b)` and the iteration number at which the method stopped or failed.

```minres(A,b,tol) ``` specifies the tolerance of the method. If `tol` is `[]`, then `minres` uses the default, `1e-6`.

```minres(A,b,tol,maxit) ``` specifies the maximum number of iterations. If `maxit` is `[]`, then `minres` uses the default, `min(n,20)`.

```minres(A,b,tol,maxit,M) and minres(A,b,tol,maxit,M1,M2) ``` use symmetric positive definite preconditioner `M` or `M = M1*M2` and effectively solve the system `inv(sqrt(M))*A*inv(sqrt(M))*y = inv(sqrt(M))*b` for `y` and then return `x = inv(sqrt(M))*y`. If `M` is `[]` then `minres` applies no preconditioner. `M` can be a function that returns `M\x`.

```minres(A,b,tol,maxit,M1,M2,x0) ``` specifies the initial guess. If `x0` is `[]`, then `minres` uses the default, an all-zero vector.

```minres(afun,b,tol,maxit,m1fun,m2fun,x0,p1,p2,...) ``` passes parameters `p1,p2,...` to functions `afun(x,p1,p2,...)`, `m1fun(x,p1,p2,...)`, and `m2fun(x,p1,p2,...)`.

```[x,flag] = minres(A,b,...) ``` also returns a convergence flag.

 Flag Convergence `0` `minres `converged to the desired tolerance `tol` within `maxit `iterations. `1` `minres` iterated `maxit` times but did not converge. `2` Preconditioner `M` was ill-conditioned. `3` `minres` stagnated. (Two consecutive iterates were the same.) `4` One of the scalar quantities calculated during `minres `became too small or too large to continue computing.

Whenever `flag` is not `0`, the solution `x` returned is that with minimal norm residual computed over all the iterations. No messages are displayed if the `flag` output is specified.

```[x,flag,relres] = minres(A,b,...) ``` also returns the relative residual `norm(b-A*x)/norm(b)`. If `flag` is `0`, `relres <= tol`.

```[x,flag,relres,iter] = minres(A,b,...) ``` also returns the iteration number at which `x` was computed, where `0 <= iter <= maxit`.

```[x,flag,relres,iter,resvec] = minres(A,b,...) ``` also returns a vector of estimates of the `minres` residual norms at each iteration, including `norm(b-A*x0)`.

```[x,flag,relres,iter,resvec,resveccg] = minres(A,b,...) ``` also returns a vector of estimates of the Conjugate Gradients residual norms at each iteration.

Examples

Example 1.

• ```n = 100; on = ones(n,1);
A = spdiags([-2*on 4*on -2*on],-1:1,n,n);
b = sum(A,2);
tol = 1e-10;
maxit = 50;
M1 = spdiags(4*on,0,n,n);

x = minres(A,b,tol,maxit,M1,[],[]);
minres converged at iteration 49 to a solution with relative
residual 4.7e-014
```

Alternatively, use this matrix-vector product function

• ```function y = afun(x,n)
y = 4 * x;
y(2:n) = y(2:n) - 2 * x(1:n-1);
y(1:n-1) = y(1:n-1) - 2 * x(2:n);
```

as input to `minres`.

• ```x1 = minres(@afun,b,tol,maxit,M1,[],n);
```

Example 2.

Use a symmetric indefinite matrix that fails with `pcg`.

• ```A = diag([20:-1:1, -1:-1:-20]);
b = sum(A,2);      % The true solution is the vector of all ones.
x = pcg(A,b);      % ```Errors out at the first iteration.
```pcg stopped at iteration 1 without converging to the desired
tolerance 1e-006 because a scalar quantity became too small or
too large to continue computing.
The iterate returned (number 0) has relative residual 1
```

However, `minres` can handle the indefinite matrix `A`.

• ```x = minres(A,b,1e-6,40);
minres converged at iteration 39 to a solution with relative
residual 1.3e-007
```

`bicg`, `bicgstab`, `cgs`, `cholinc`, `gmres`, `lsqr`, `pcg`, `qmr`, `symmlq`
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