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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

Keywords: minimize, quasi-newton, variable metric
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()

Gary Oehlert 2003-01-15