Study on a supermemory gradient method for the minimization of functions

A new accelerated gradient method for finding the minimum of a functionf(x) whose variables are unconstrained is investigated. The new algorithm can be stated as follows: $$\tilde x = x + \delta x,\delta x = - \alpha g(x) + \sum\limits_{i = 1}^k {\beta _i \delta x_i }$$ wherex is ann-vector,g(x) is the gradient of the functionf(x), δx is the change in the position vector for the iteration under consideration, and δx _i is the change in the position vector for theith previous iteration. The quantities α and β _i are scalars chosen at each step so as to yield the greatest decrease in the function; the scalark denotes the number of past iterations remembered. Fork=1, the algorithm reduces to the memory gradient method of Ref. 2; it contains at most two undetermined multipliers to be optimized by a two-dimensional search. Fork=n?1, the algorithm contains at mostn undetermined multipliers to be optimized by ann-dimensional search. Two nonquadratic test problems are considered. For both problems, the memory gradient method and the supermemory gradient method are compared with the Fletcher-Reeves method and the Fletcher-Powell-Davidon method. A comparison with quasilinearization is also presented.