Scudd PSA

This app is developed to determine the unconstrained minimum of a function of several variables without calculating their derivatives. The app is derived from Simulated Annealing (SA) algorithm recently introduced in combinatorial optimization. The algorithm is an iterative random search procedure with adaptive moves along the coordinate directions. It permits uphill moves under the control of a probabilistic criterion, thus, tending to avoid the first local minima encountered.
The app has been tested against Nelder and Mead simplex method and against the basic SA algorithm, the test functions were Rosenbrock valley in two dimension, Powell's quartic function in 4D, and multiminima function in 6 and 10 dimensions.
The app proved to be more reliable than the others, being always able to find the optimum, or at least a point very close to it. The number of iterations at each temperature during the cooling was able to determine. The cooling factor in the SA cooling schedule was found to be a variable and not a constant as proposed by many other authors.