Abstract: | This paper develops convergence theory of the gradient projection method by Calamai and Moré (Math. Programming, vol. 39, 93–116, 1987) which, for minimizing a continuously differentiable optimization problem min{f(x) : x } where is a nonempty closed convex set, generates a sequence xk+1 = P(xk – k f(xk)) where the stepsize k > 0 is chosen suitably. It is shown that, when f(x) is a pseudo-convex (quasi-convex) function, this method has strong convergence results: either xk x* and x* is a minimizer (stationary point); or xk arg min{f(x) : x } = , and f(xk) inf{f(x) : x }. |