Generalized minimal residual method

In mathematics, the generalized minimal residual method (usually abbreviated GMRES) is an iterative method for the numerical solution of a nonsymmetric system of linear equations. The method approximates the solution by the vector in a Krylov subspace with minimal residual. The Arnoldi iteration is used to find this vector.

The GMRES method was developed by Yousef Saad and Martin H. Schultz in 1986.^[1] GMRES is a generalization of the MINRES method developed by Chris Paige and Michael Saunders in 1975. GMRES also is a special case of the DIIS method developed by Peter Pulay in 1980. DIIS is also applicable to non-linear systems.

The method

Denote the Euclidean norm of any vector v by $\|v\|$ . Denote the (square) system of linear equations to be solved by

Ax=b.\,

The matrix A is assumed to be invertible of size m-by-m. Furthermore, it is assumed that b is normalized, i.e., that $\|b\|=1$ .

The n-th Krylov subspace for this problem is

K_{n}=K_{n}(A,b)=\operatorname {span}\,\{b,Ab,A^{2}b,\ldots ,A^{{n-1}}b\}.\,

GMRES approximates the exact solution of $Ax=b$ by the vector $x_n \in K_n$ that minimizes the Euclidean norm of the residual $r_n= Ax_n-b$ .

The vectors $b,Ab,\ldots A^{n-1}b$ might be close to linearly dependent, so instead of this basis, the Arnoldi iteration is used to find orthonormal vectors $q_{1},q_{2},\ldots ,q_{n}\,$ which form a basis for $K_{n}$ . Hence, the vector $x_n \in K_n$ can be written as $x_n = Q_n y_n$ with $y_n \in \mathbb{R}^n$ , where $Q_n$ is the m-by-n matrix formed by $q_1,\ldots,q_n$ .

The Arnoldi process also produces an ( $n+1$ )-by- $n$ upper Hessenberg matrix ${\tilde {H}}_{n}$ with

AQ_{n}=Q_{{n+1}}{\tilde {H}}_{n}.\,

Because columns of $Q_{n}$ are orthogonal, we have

\|Ax_{n}-b\|=\|{\tilde {H}}_{n}y_{n}-Q_{n+1}^{T}b\|=\|{\tilde {H}}_{n}y_{n}-\beta e_{1}\|,\,

where

e_{1}=(1,0,0,\ldots ,0)^{T}\,

is the first vector in the standard basis of $\mathbb{R}^{n+1}$ , and

\beta =\|b-Ax_{0}\|\,,

$x_{0}$ being the first trial vector (usually zero). Hence, $x_{n}$ can be found by minimizing the Euclidean norm of the residual

r_{n}={\tilde {H}}_{n}y_{n}-\beta e_{1}.

This is a linear least squares problem of size n.

This yields the GMRES method. On the $n$ -th iteration:

calculate $q_{n}$ with the Arnoldi method;
find the $y_n$ which minimizes $\|r_{n}\|$ ;
compute $x_{n}=Q_{n}y_{n}$ ;
repeat if the residual is not yet small enough.

At every iteration, a matrix-vector product $A q_n$ must be computed. This costs about $2m^2$ floating-point operations for general dense matrices of size $m$ , but the cost can decrease to $O(m)$ for sparse matrices. In addition to the matrix-vector product, $O(nm)$ floating-point operations must be computed at the n -th iteration.

Convergence

The nth iterate minimizes the residual in the Krylov subspace K_n. Since every subspace is contained in the next subspace, the residual decreases monotonically. After m iterations, where m is the size of the matrix A, the Krylov space K_m is the whole of R^m and hence the GMRES method arrives at the exact solution. However, the idea is that after a small number of iterations (relative to m), the vector x_n is already a good approximation to the exact solution.

This does not happen in general. Indeed, a theorem of Greenbaum, Pták and Strakoš states that for every monotonically decreasing sequence a₁, …, a_m−1, a_m = 0, one can find a matrix A such that the ||r_n|| = a_n for all n, where r_n is the residual defined above. In particular, it is possible to find a matrix for which the residual stays constant for m − 1 iterations, and only drops to zero at the last iteration.

In practice, though, GMRES often performs well. This can be proven in specific situations. If the symmetric part of A, that is $1/2(A^{T}+A)$ , is positive definite, then

\|r_{n}\|\leq \left(1-{\frac {\lambda _{{{\mathrm {min}}}}^{2}(1/2(A^{T}+A))}{\lambda _{{{\mathrm {max}}}}(A^{T}A)}}\right)^{{n/2}}\|r_{0}\|,

where $\lambda _{{{\mathrm {min}}}}(M)$ and $\lambda _{{{\mathrm {max}}}}(M)$ denote the smallest and largest eigenvalue of the matrix $M$ , respectively.^[2]

If A is symmetric and positive definite, then we even have

\|r_{n}\|\leq \left({\frac {\kappa _{2}(A)^{2}-1}{\kappa _{2}(A)^{2}}}\right)^{n/2}\|r_{0}\|.

where $\kappa _{2}(A)$ denotes the condition number of A in the Euclidean norm.

In the general case, where A is not positive definite, we have

\|r_{n}\|\leq \inf _{{p\in P_{n}}}\|p(A)\|\leq \kappa _{2}(V)\inf _{{p\in P_{n}}}\max _{{\lambda \in \sigma (A)}}|p(\lambda )|\|r_{0}\|,\,

where P_n denotes the set of polynomials of degree at most n with p(0) = 1, V is the matrix appearing in the spectral decomposition of A, and σ(A) is the spectrum of A. Roughly speaking, this says that fast convergence occurs when the eigenvalues of A are clustered away from the origin and A is not too far from normality.^[3]

All these inequalities bound only the residuals instead of the actual error, that is, the distance between the current iterate x_n and the exact solution.

Extensions of the method

Like other iterative methods, GMRES is usually combined with a preconditioning method in order to speed up convergence.

The cost of the iterations grow as O(n²), where n is the iteration number. Therefore, the method is sometimes restarted after a number, say k, of iterations, with x_k as initial guess. The resulting method is called GMRES(k) or Restarted GMRES. This methods suffers from stagnation in convergence as the restarted subspace is often close to the earlier subspace.

The shortcomings of GMRES and restarted GMRES are addressed by the recycling of Krylov subspace in the GCRO type methods such as GCROT and GCRODR.^[4] Recycling of Krylov subspaces in GMRES can also speed up convergence when sequences of linear systems need to be solved.^[5]

Comparison with other solvers

The Arnoldi iteration reduces to the Lanczos iteration for symmetric matrices. The corresponding Krylov subspace method is the minimal residual method (MinRes) of Paige and Saunders. Unlike the unsymmetric case, the MinRes method is given by a three-term recurrence relation. It can be shown that there is no Krylov subspace method for general matrices, which is given by a short recurrence relation and yet minimizes the norms of the residuals, as GMRES does.

Another class of methods builds on the unsymmetric Lanczos iteration, in particular the BiCG method. These use a three-term recurrence relation, but they do not attain the minimum residual, and hence the residual does not decrease monotonically for these methods. Convergence is not even guaranteed.

The third class is formed by methods like CGS and BiCGSTAB. These also work with a three-term recurrence relation (hence, without optimality) and they can even terminate prematurely without achieving convergence. The idea behind these methods is to choose the generating polynomials of the iteration sequence suitably.

None of these three classes is the best for all matrices; there are always examples in which one class outperforms the other. Therefore, multiple solvers are tried in practice to see which one is the best for a given problem.

Solving the least squares problem

One part of the GMRES method is to find the vector $y_{n}$ which minimizes

\|{\tilde {H}}_{n}y_{n}-\beta e_{1}\|.\,

Note that ${\tilde {H}}_{n}$ is an (n+1)-by-n matrix, hence it gives an over-constrained linear system of n+1 equations for n unknowns.

The minimum can be computed using a QR decomposition: find an (n+1)-by-(n+1) orthogonal matrix Ω_n and an (n+1)-by-n upper triangular matrix ${\tilde {R}}_{n}$ such that

\Omega _{n}{\tilde {H}}_{n}={\tilde {R}}_{n}.

The triangular matrix has one more row than it has columns, so its bottom row consists of zero. Hence, it can be decomposed as

{\tilde {R}}_{n}={\begin{bmatrix}R_{n}\\0\end{bmatrix}},

where $R_{n}$ is an n-by-n (thus square) triangular matrix.

The QR decomposition can be updated cheaply from one iteration to the next, because the Hessenberg matrices differ only by a row of zeros and a column:

{\tilde {H}}_{{n+1}}={\begin{bmatrix}{\tilde {H}}_{n}&h_{{n+1}}\\0&h_{{n+2,n+1}}\end{bmatrix}},

where h_n+1 = (h_1,n+1, …, h_n+1,n+1)^T. This implies that premultiplying the Hessenberg matrix with Ω_n, augmented with zeroes and a row with multiplicative identity, yields almost a triangular matrix:

{\begin{bmatrix}\Omega _{n}&0\\0&1\end{bmatrix}}{\tilde {H}}_{{n+1}}={\begin{bmatrix}R_{n}&r_{{n+1}}\\0&\rho \\0&\sigma \end{bmatrix}}

This would be triangular if σ is zero. To remedy this, one needs the Givens rotation

G_{n}={\begin{bmatrix}I_{{n}}&0&0\\0&c_{n}&s_{n}\\0&-s_{n}&c_{n}\end{bmatrix}}

where

c_{n}={\frac {\rho }{{\sqrt {\rho ^{2}+\sigma ^{2}}}}}\quad {\mbox{and}}\quad s_{n}={\frac {\sigma }{{\sqrt {\rho ^{2}+\sigma ^{2}}}}}.

With this Givens rotation, we form

\Omega _{{n+1}}=G_{n}{\begin{bmatrix}\Omega _{n}&0\\0&1\end{bmatrix}}.

Indeed,

\Omega _{{n+1}}{\tilde {H}}_{{n+1}}={\begin{bmatrix}R_{n}&r_{{n+1}}\\0&r_{{n+1,n+1}}\\0&0\end{bmatrix}}\quad {\text{with}}\quad r_{{n+1,n+1}}={\sqrt {\rho ^{2}+\sigma ^{2}}}

is a triangular matrix.

Given the QR decomposition, the minimization problem is easily solved by noting that

\|{\tilde {H}}_{n}y_{n}-\beta e_{1}\|=\|\Omega _{n}({\tilde {H}}_{n}y_{n}-\beta e_{1})\|=\|{\tilde {R}}_{n}y_{n}-\beta \Omega _{n}e_{1}\|.

Denoting the vector $\beta \Omega _{n}e_{1}$ by

{\tilde {g}}_{n}={\begin{bmatrix}g_{n}\\\gamma _{n}\end{bmatrix}}

with g_n ∈ Rⁿ and γ_n ∈ R, this is

\|{\tilde {H}}_{n}y_{n}-\beta e_{1}\|=\|{\tilde {R}}_{n}y_{n}-\beta \Omega _{n}e_{1}\|=\left\|{\begin{bmatrix}R_{n}\\0\end{bmatrix}}y_{n}-{\begin{bmatrix}g_{n}\\\gamma _{n}\end{bmatrix}}\right\|.

The vector y that minimizes this expression is given by

y_{n}=R_{n}^{{-1}}g_{n}.

Again, the vectors $g_{n}$ are easy to update.^[6]

Example code

Regular GMRES (MATLAB / GNU Octave)

function [x, e] = gmres( A, b, x, max_iterations, threshold)
  n = length(A);
  m = max_iterations;
  
  %use x as the initial vector
  r=b-A*x;

  b_norm = norm(b);
  error = norm(r)/b_norm;

  %initialize the 1D vectors
  sn = zeros(m,1);
  cs = zeros(m,1);
  e1 = zeros(n,1);
  e1(1) = 1;
  e=[error];
  r_norm=norm(r);
  Q(:,1) = r/r_norm;
  beta = r_norm*e1;
  for k = 1:m                                   
    
    %run arnoldi
    [H(1:k+1,k) Q(:,k+1)] = arnoldi(A, Q, k);
    
    %eliminate the last element in H ith row and update the rotation matrix
    [H(1:k+1,k) cs(k) sn(k)] = apply_givens_rotation(H(1:k+1,k), cs, sn, k);
    
    %update the residual vector
    beta(k+1) = -sn(k)*beta(k);
    beta(k)   = cs(k)*beta(k);
    error  = abs(beta(k+1)) / b_norm;
    
    %save the error
    e=[e; error];
    
    if ( error <= threshold)
      break;
    end
  end

  %calculate the result
  y = H(1:k,1:k) \ beta(1:k);
  x = x + Q(:,1:k)*y; 
end

%----------------------------------------------------%
%                  Arnoldi Function                  %
%----------------------------------------------------%
function [h q] = arnoldi(A, Q, k)
  q = A*Q(:,k);
  for i = 1:k,
    h(i)= q'*Q(:,i);
    q = q - h(i)*Q(:,i);
  end
  h(k+1) = norm(q);
  q = q / h(k+1);
end

%---------------------------------------------------------------------%
%                  Applying Givens Rotation to H col                  %
%---------------------------------------------------------------------%
function [h, cs_k sn_k] = apply_givens_rotation(h, cs, sn, k)
  %apply for ith column
  for i = 1:k-1,                              
    temp     =  cs(i)*h(i) + sn(i)*h(i+1);
    h(i+1) = -sn(i)*h(i) + cs(i)*h(i+1);
    h(i)   = temp;
  end
  
  %update the next sin cos values for rotation
  [cs_k sn_k] = givens_rotation( h(k), h(k+1));
  
  %eliminate H(i+1,i)
  h(k) = cs_k*h(k) + sn_k*h(k+1);
  h(k+1) = 0.0;
end

%%----Calculate the Given rotation matrix----%%
function [cs sn] = givens_rotation(v1, v2)
  if (v1==0)
    cs = 0;
    sn = 1;
  else
    t=sqrt(v1^2+v2^2);
    cs = abs(v1) / t;
    sn = cs * v2 / v1;
  end
end

References

↑ Y. Saad and M.H. Schultz
↑ Eisenstat, Elman & Schultz, Thm 3.3. NB all results for GCR also hold for GMRES, cf. Saad & Schultz
↑ Trefethen & Bau, Thm 35.2
↑ Amritkar, Amit; de Sturler, Eric; Świrydowicz, Katarzyna; Tafti, Danesh; Ahuja, Kapil (2015). "Recycling Krylov subspaces for CFD applications and a new hybrid recycling solver". Journal of Computational Physics. 303: 222. doi:10.1016/j.jcp.2015.09.040.
↑ Gaul, André (2014). Recycling Krylov subspace methods for sequences of linear systems (Ph.D.). TU Berlin. doi:10.14279/depositonce-4147.
↑ Stoer and Bulirsch, §8.7.2

Notes

A. Meister, Numerik linearer Gleichungssysteme, 2nd edition, Vieweg 2005, ISBN 978-3-528-13135-7.
Y. Saad, Iterative Methods for Sparse Linear Systems, 2nd edition, Society for Industrial and Applied Mathematics, 2003. ISBN 978-0-89871-534-7.
Y. Saad and M.H. Schultz, "GMRES: A generalized minimal residual algorithm for solving nonsymmetric linear systems", SIAM J. Sci. Stat. Comput., 7:856-869, 1986. doi:10.1137/0907058.
S. C. Eisenstat, H.C. Elman and M.H. Schultz, "Variational iterative methods for nonsymmetric systems of linear equations", SIAM Journal on Numerical Analysis, 20(2), 345-357, 1983.
J. Stoer and R. Bulirsch, Introduction to numerical analysis, 3rd edition, Springer, New York, 2002. ISBN 978-0-387-95452-3.
Lloyd N. Trefethen and David Bau, III, Numerical Linear Algebra, Society for Industrial and Applied Mathematics, 1997. ISBN 978-0-89871-361-9.
Dongarra et al. , Templates for the Solution of Linear Systems: Building Blocks for Iterative Methods, 2nd Edition, SIAM, Philadelphia, 1994
Amritkar, Amit; de Sturler, Eric; Świrydowicz, Katarzyna; Tafti, Danesh; Ahuja, Kapil (2015). "Recycling Krylov subspaces for CFD applications and a new hybrid recycling solver". Journal of Computational Physics 303: 222. doi:10.1016/j.jcp.2015.09.040

Numerical linear algebra
Key concepts	Floating point Numerical stability
Problems	Matrix multiplication (algorithms) Matrix decompositions Linear equations Sparse problems
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