SVD as a preconditioner in nonlinear optimization

Finding a solution of nonlinear constrained optimization problem may be very computer resources consuming, regardless of solution method adopted. A conceptually simple preconditioning procedure, based on singular value decomposition (SVD), is proposed in the current paper in order to speed up the convergence of a gradient based algorithm to solve constrained minimization problem having quadratic objective function. The efficiency of the proposed procedure is tested on a constrained minimization problem with quadratic objective function and quadratic constraints. Accuracy of the results obtained using proposed preconditioning method is checked and verified against the results determined without the preconditioning procedure. Results obtained so far seem to indicate a significant speedup of the calculations at the expense of, negligible from the engineering point of view, loss of accuracy.