Vectorization of conjugate-gradient methods for large-scale minimization

Vectorization techniques are applied to the nonlinear conjugate-gradient method for large-scale unconstrained minimization. Computational results are presented for a robust limited-memory quasi-Newton-like conjugate-gradient algorithm applied to meteorological problems. The vectorization results in speedups up to a factor of 21 compared to the performance of the scalar code, when nonlinear functions of 10⁴-10⁵ variables are minimized. A sizable reduction in the CPU time required for the minimization of large-scale nonlinear functions is obtained, showing the advantages of the approach