A family of parallel quasi-Newton algorithms for unconstrained minimization

This paper deals with the solution of the unconstrained optimization problems on parallel computers using quasi-Newton methods. The algorithm is based on that parallelism can be exploited in function and derivative evaluation costs and linear algebra calculations in the standard sequential algorithm. Computational problem is reported for showing that the parallel algorithm is superior to the sequential one.