bfs(x0, fun [,params:params] [, goldsteps:ngold] [, maxit:maxiter]\ [,minit:miniter] [,criteria:vector(nsigx,nsigfun,dgrad)]\ [printwhen:d1] [,recordwhen:d2]), REAL vector x0, macro fun(x,i [,params]), integers ngold > 0, maxiter >= 0, miniter > 0, nsigx, nsigfun, d1 >= 0, d2 >= 0, dgrad REAL scalar |
Macro bfs() uses the Broyden-Fletcher-Shanno variable metric algorithm to minimize a function iteratively. A golden mean line search is made at each step. See Dahlquist and Bjorck, Numerical methods, Prentice Hall, 1974, p. 443. bfs() is a "front-end" to macro minimizer() which it calls with all the arguments to bfs() plus argument 'method:"bfs"'. result <- bfs(x0, fun [, params] [,optional keywords]) computes the minimum of a real function F(x1,x2,...,xk) starting the Broyden- Fletcher-Shanno iteration at x = x0 = vector(x01,x02,...,x0k), a REAL vector with no MISSING elements. See minimizer() for details on the arguments, keywords and the value. See also mnimizer(), dfp(), broyden(), and neldermead()