BBVSCG - A variable-storage algorithm for function minimization This routine is designed to find a close approximation to a local minimum of a nonlinear function f(x). Here x is a vector of n variables, that is, $x=(xsb 1,xsb 2,...,xsb n),$ and f is assumed to be smooth, that is, to have at least continuous second derivatives. As with almost all minimization algorithms, there is no attempt made to ensure that the minimum obtained is global. The algorithm is based on an earlier algorithm, namely, CONMIN, due to {it D. F. Shanno} and {it K. H. Phua} [Minimization of unconstrained multivariate functions, ibid. 6, 618-622 (1980)], but offers a fundamental facility which was not available in CONMIN. In the CONMIN code, one could either use a conjugate gradient code if little storage was available, or a quasi-Newton code if there was sufficient storage. BBVSCG offers the user the opportunity to specify the amount of available storage; the code then chooses an appropriate algorithm.