这件事情起因源于美国的Nancy同学，涉及到一个求函数最小值的问题，然后就牵涉到S-Plus中nlmin函数，现将S-Plus中关于nlmin函数的reference粘贴如下：
**nlmin** S-PLUS Language Reference
**Find Local Minimum of a Nonlinear Function**
**DESCRIPTION:**
Finds a local minimum of a nonlinear function using a **general quasi-Newton optimizer**.
**USAGE:**<PRE> nlmin(f, x, d=rep(1,length(x)), print.level=0, max.fcal=30,
max.iter=15, init.step=1, rfc.tol=<<see below>>, ckfc=0.1,
xc.tol=<<see below>>, xf.tol=<<see below>>) </PRE>
**REQUIRED ARGUMENTS:**
f
an S-PLUS function which takes as its only argument a real vector of length p. The function must return a value of storage mode "double".
x
a vector of length p used as the starting point for the optimizer.
**OPTIONAL ARGUMENTS: **
d
a vector of length p used as a scaling vector for x. The elements of the vector d* x should be in comparable units. Normally d is only specified if the elements of x have differing orders of magnitude.
print.level=
if print.level=0, then all output from the optimizer is suppressed. If print.level=1, then a short summary of each iteration is printed. If print.level=2, then a long summary of each iteration is printed.
max.fcal=
the maximum number of function evaluations permitted.
max.iter=
the maximum number of iterations permitted.
init.step=
a bound on the L2-norm of the initial scaled step. This can have a dramatic effect on the performance of the optimizer.
rfc.tol=
the relative function convergence tolerance. If the predicted function reduction is no more than rfc.tol times |f|, where |f| is the function value, then the optimizer has converged. The default is max(10^-10, e^(2/3)), where e is machine epsilon.
ckfc=
if the predicted function decrease is smaller than ckfc times the actual function decrease, then a check is performed for false convergence.
xc.tol=
the relative x-convergence tolerance, where x is the argument of the function being minimized. If a Newton step has a scaled step length smaller than xc.tol, then the optimizer converges. The default is sqrt(e) where e is machine epsilon.
xf.tol=
the false convergence tolerance. If relative function convergence and relative x-convergence were not achieved, and a Newton step has scaled step length smaller than xf.tol, then the optimizer appears to be stopped at a non-critical solution. The default is 100 times the machine epsilon.
**VALUE: **
a list with the following components:
x
the value at which the optimizer has converged
converged
if the optimizer has apparently successfully converged to a minimum, then converged is TRUE; otherwise converged is FALSE.
conv.type
a character string describing the type of convergence.
DETAILS:
The optimizer is based on a quasi-Newton method using the double dogleg step with the BFGS secant update to the Hessian. See Dennis, Gay, and Welsch (1981) and Dennis and Mei (1979) for details.
**BUGS:**
If the storage mode of the return value of f is "single" or "integer", nlmin will not work properly (but converged will be FALSE).
**REFERENCES: **
Dennis, J. E., Gay, D. M., and Welsch, R. E. (1981). An adaptive nonlinear least-squares algorithm. ACM Transactions on Mathematical Software 7, 348-383.
Dennis, J. E. and Mei, H. H. W. (1979). Two new unconstrained optimization algorithms which use function and gradient values. Journal of Optimization Theory and Applications 28, 453-483.
**SEE ALSO: **
nlminb , arima.mle .
**EXAMPLES: **
# minimize a simple function
func <- function(x) {x^2-2*x+4}
min.func <- nlmin(func,0)
# One way to pass parameters to the function is:
function()
{
co <- c(1, 2)
assign("co", co, frame = 1)
f1 <- function(x) co[1] + co[2] * x^2
nlmin(f1, 10)
}
# Another way to pass parameters is by setting the default
# value for an argument; e.g. g$y0 in next example:# nlmin can be used to solve a system of nonlinear equations:
solveNonlinear <- function(f, y0, x, ...){
# solve f(x) = y0
# x is vector of initial guesses, same length as y0
# ... are additional arguments to nlmin (not to f)
g <- function(x, y0, f) sum((f(x) - y0)^2)
g$y0 <- y0 # set g's default value for y0
g$f <- f # set g's default value for f
nlmin(g, x, ...)
}
f <- function(x) c(exp(x[1]), sin(x[2]))
solveNonlinear(f, c( 3, .5), c(.2,.2)) # solve exp(x1)=3, sin(x2)=.5
# result$x is c(log(3), asin(.5))
g <- function(x) exp(x[1]) * c(1, sin(x[2]))
solveNonlinear(g, c( 3, .5), c(.2,.2)) # solve exp(x1)=3, exp(x1)sin(x2)=.5
# Note that multiple variables (x1 and x2) can be thought of as components
# of a single multivariate variable (x[1] and x[2]), and
# that multiple functions (exp(x1) and exp(x1)*sin(x2))
# correspond to a vector-valued function c(exp(x1), exp(x1)*sin(x2))
# Warning -- solveNonlinear can be fooled; it minimizes a sum of
# squares, and may stop if it believes the SS is not decreasing,
# even if not the SS is zero.
感慨一句，统计计算（数值计算 / 数值分析 / Computational Statistics / Numerical Analysis）真是一门恐怖的学问。