  An algorithm capable of finding a likely global optimum (minimum) and a set
of sub-optimal points for arbitrary generic functions of several variables is
presented. The algorithm is designed to deal even with functions of complex
behavior, irregular and noisy, with steep variations and exhibiting a lot of
local sub-optimal points. The complications of having to deal with a finite
domain, as this is usually the case, are taken into account. The method is
composed of a number of cascaded stages, each employing a different strategy to
improve over the results of the previous stage. Many ideas and concepts
employed in known methods are re-elaborated in a coherent scheme, plus several
new ideas are introduced. Line minimization plays an important role in most
stages, for this purpose a new and powerful algorithm for line minimization is
used as well.
