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**Computing and deflating eigenvalues while solving multiple right-hand side linear systems with an application to quantum chromodynamics.**
*(English)*
Zbl 1209.65046

Summary: We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive definite matrix while solving a linear system of equations with conjugate gradient (CG). Traditionally, all the CG iteration vectors could be saved and recombined through the eigenvectors of the tridiagonal projection matrix, which is equivalent theoretically to unrestarted Lanczos. Our algorithm capitalizes on the iteration vectors produced by CG to update only a small window of vectors that approximate the eigenvectors.

While this window is restarted in a locally optimal way, the CG algorithm for the linear system is unaffected. Yet, in all our experiments, if the window has more than a properly chosen but small number of vectors, it converges to the required eigenvectors at a rate identical to unrestarted Lanczos. After the solution of the linear system, eigenvectors that have not accurately converged can be improved in an incremental fashion by solving additional linear systems. In this case, eigenvectors identified in earlier systems can be used to deflate, and thus accelerate, the convergence of subsequent systems.

We have used this algorithm with excellent results in lattice quantum chromodynamics applications, where hundreds of right-hand sides may be needed. Specifically, about 70 eigenvectors are obtained to full accuracy after solving 24 right-hand sides. Deflating these from the large number of subsequent right-hand sides removes the dreaded critical slowdown, where the conditioning of the matrix increases as the quark mass reaches a critical value. Our experiments show almost a constant number of iterations for our method, regardless of quark mass, and speedups of 8 over original CG for light quark masses.

While this window is restarted in a locally optimal way, the CG algorithm for the linear system is unaffected. Yet, in all our experiments, if the window has more than a properly chosen but small number of vectors, it converges to the required eigenvectors at a rate identical to unrestarted Lanczos. After the solution of the linear system, eigenvectors that have not accurately converged can be improved in an incremental fashion by solving additional linear systems. In this case, eigenvectors identified in earlier systems can be used to deflate, and thus accelerate, the convergence of subsequent systems.

We have used this algorithm with excellent results in lattice quantum chromodynamics applications, where hundreds of right-hand sides may be needed. Specifically, about 70 eigenvectors are obtained to full accuracy after solving 24 right-hand sides. Deflating these from the large number of subsequent right-hand sides removes the dreaded critical slowdown, where the conditioning of the matrix increases as the quark mass reaches a critical value. Our experiments show almost a constant number of iterations for our method, regardless of quark mass, and speedups of 8 over original CG for light quark masses.

### MSC:

65F15 | Numerical computation of eigenvalues and eigenvectors of matrices |

81V05 | Strong interaction, including quantum chromodynamics |