A NEW UNCONSTRAINED OPTIMIZATION METHOD FOR IMPRECISE FUNCTION AND GRADIENT VALUES

A new algorithm for unconstrained optimization is presented which is b ased on a modified one-dimensional bisection method. The algorithm act ually uses only the signs of function and gradient values. Thus it can be applied to problems with imprecise function and gradient values. I t converges in one iteration on quadratic functions of n variables, it rapidly minimizes general functions and it does not require evaluatio n or estimation of the matrix of second partial derivatives. The algor ithm has been implemented and tested. Performance information for well -known test functions is reported. (C) 1996 Academic Press, Inc.