* Use classical Gram-Schmidt algorithm with two orthogonalization
* iterations and delayed orthogonalization using the algorithm described
* in @cite Bielich2022. This approach works on multi-vectors with a
- * single global reduction (of multiple elements) and more efficient than
- * the modified Gram-Schmidt algorithm. At the same time, it
+ * single global reduction (of multiple elements) and is more efficient
+ * than the modified Gram-Schmidt algorithm. At the same time, it
* unconditionally performs the second orthogonalization step, making it
* more stable than the classical Gram-Schmidt variant. For deal.II's own
* vectors, there is no additional cost compared to the classical
/**
* Estimates the eigenvalues from the Hessenberg matrix, H_orig, generated
* during the inner iterations for @p n vectors in total. Uses these
- * estimate to compute the condition number. Calls the signals
+ * estimates to compute the condition number. Calls the signals
* eigenvalues_signal and cond_signal with these estimates as arguments.
*/
static void
// one where the orthogonalization has finished (i.e., end of inner
// iteration in GMRES) and we can safely overwrite the content of the
// tridiagonal matrix and right hand side, and the case during the inner
- // iterations where need to create copies of the matrices in the QR
+ // iterations where we need to create copies of the matrices in the QR
// decomposition as well as the right hand side.
if (orthogonalization_strategy ==
LinearAlgebra::OrthogonalizationStrategy::