Why MATLAB chooses the Householder in its built-in function gmres.m?

Recently, I have studied how to construct an orthonormal basis for Krylov subspace to solve $$Ax=b$$, where $$A\in \mathbb{R}^{n\times n}$$ is nonsingular. As we know, there are usually 4 ways to construct an $$m$$ dimensional Krylov subspace $$\{v,Av,...,A^{m-1}v\}$$ as follow:

1. standard Gram-Schmidt (G-S);
2. Modified Gram-Schmidt (MGS);
3. Householder reflection (House);
4. MGS with reorthogonalization (MGSR).

$$\begin{array}{|c|c|c|} \hline \textbf{Method} & \textbf{Work} & \textbf{Storage} \\ \hline \text{G-S} & 2m^2n & (m+1)n \\ \hline \text{MGS} & 2m^2n & (m+1)n \\ \hline \text{House} & 4m^2n-\frac{4}{3}m^3& (m+1)n-\frac{1}{2}m^2 \\ \hline \text{MGSR} & 4m^2n & (m+1)n \\ \hline \end{array}$$

My question is from the computational work, the MGSR is most about twice than G-S and MGS. From the numerical stability point, Householder is the most reliable method, but the computational work is very much. For us, if given any matrix $$A$$, how should we choose the best algorithm to write the method, e.g., gmres.m? Is there a criterion? By the way, I find my Matlab 2018b chooses the Householder method. Does this mean that in practice, Householder is proved the best efficient and stable method?

• Do the GS and MGS costs include double orthogonalization? – Federico Poloni Dec 14 '19 at 8:25
• No, Gram-Schmidt and Midified GS do not include orthogonalization. – sunshine Dec 14 '19 at 12:23