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Solve system of linear equations — transpose-free quasi-minimal residual method

attempts to solve the system of linear equations `x`

= tfqmr(`A`

,`b`

)`A*x = b`

for
`x`

using the Transpose-free Quasi-minimal Residual Method. When the attempt is
successful, `tfqmr`

displays a message to confirm convergence. If
`tfqmr`

fails to converge after the maximum number of iterations or
halts for any reason, it displays a diagnostic message that includes the relative residual
`norm(b-A*x)/norm(b)`

and the iteration number at which the method
stopped.

`[`

returns a flag that specifies whether the algorithm successfully converged. When
`x`

,`flag`

] = tfqmr(___)`flag = 0`

, convergence was successful. You can use this output syntax
with any of the previous input argument combinations. When you specify the
`flag`

output, `tfqmr`

does not display any diagnostic
messages.

Convergence of most iterative methods depends on the condition number of the coefficient matrix,

`cond(A)`

. You can use`equilibrate`

to improve the condition number of`A`

, and on its own this makes it easier for most iterative solvers to converge. However, using`equilibrate`

also leads to better quality preconditioner matrices when you subsequently factor the equilibrated matrix`B = R*P*A*C`

.You can use matrix reordering functions such as

`dissect`

and`symrcm`

to permute the rows and columns of the coefficient matrix and minimize the number of nonzeros when the coefficient matrix is factored to generate a preconditioner. This can reduce the memory and time required to subsequently solve the preconditioned linear system.

[1] Barrett, R., M. Berry, T. F. Chan,
et al., *Templates for the Solution of Linear Systems: Building Blocks for Iterative
Methods*, SIAM, Philadelphia, 1994.

`bicg`

| `bicgstab`

| `bicgstabl`

| `cgs`

| `gmres`

| `lsqr`

| `ilu`

| `minres`

| `pcg`

| `qmr`

| `symmlq`

| `mldivide`