The aim of this short article is to show that  grid and cluster computing
provides tremendous power to optimization methods. The methods that the
article picks up are a successive convex relaxation method for quadratic
optimization problems, a polyhedral homotopy method for polynomial systems
of equations and a primal-dual interior-point method for semidefinite
programming problems.  Their parallel implementations on grids and clusters
together with numerical results are reported.