Multi-directional parallel algorithms for unconstrained optimization

Abstract

Parallel algorithms for solving unconstrained nonlinear optimization problems are presented. These algorithms are based on the quasi-Newton methods. At each step of the algorithms, several search directions are generated in parallel using various quasi-Newton updates. Our numerical results show significant improvement in the number of iterations and function evaluations required by the parallel algorithms over those required by the serial quasi-Newton updates such as the SR1 method or the BFGS method for many of the test problems.