optimize(stats)
optimize()所属R语言包：stats
One Dimensional Optimization
一维优化
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The function optimize searches the interval from lower to upper for a minimum or maximum of the function f with respect to its first argument.
功能optimize搜索间隔lowerupper最低或最高的功能f尊重其第一个参数。
optimise is an alias for optimize.
optimise是optimize别名。
用法----------Usage----------
optimize(f = , interval = , ..., lower = min(interval),
upper = max(interval), maximum = FALSE,
tol = .Machine$double.eps^0.25)
optimise(f = , interval = , ..., lower = min(interval),
upper = max(interval), maximum = FALSE,
tol = .Machine$double.eps^0.25)
参数----------Arguments----------
参数：f
the function to be optimized. The function is either minimized or maximized over its first argument depending on the value of maximum.
函数进行优化。该功能可以最小化或最大化，取决于maximum值超过其第一参数。
参数：interval
a vector containing the end-points of the interval to be searched for the minimum.
一个向量包含要搜索的最低间隔终点。
参数：...
additional named or unnamed arguments to be passed to f.
额外的命名或无名的参数被传递到f。
参数：lower
the lower end point of the interval to be searched.
低端点的间隔被搜查。
参数：upper
the upper end point of the interval to be searched.
要搜索的高端点的间隔。
参数：maximum
logical. Should we maximize or minimize (the default)?
逻辑。我们应该最大化或最小化（默认）？
参数：tol
the desired accuracy.
所需的精度。
Details
详情----------Details----------
Note that arguments after ... must be matched exactly.
请注意，后...参数必须完全匹配。
The method used is a combination of golden section search and successive parabolic interpolation, and was designed for use with continuous functions. Convergence is never much slower than that for a Fibonacci search. If f has a continuous second derivative which is positive at the minimum (which is not at lower or upper), then convergence is superlinear, and usually of the order of about 1.324.
所采用的方法是一个黄金分割搜索和连续的抛物线插值结合，为连续函数的使用而设计的。收敛是从来没有比这慢的斐波纳契搜索。 f如果有连续的二阶导数，这是最低的阳性（这是不是在lower或upper），然后融合是超，通常约1.324秩序。
The function f is never evaluated at two points closer together than eps * |x_0| + (tol/3), where eps is approximately sqrt(.Machine$double.eps) and x_0 is the final abscissa optimize()$minimum.<br> If f is a unimodal function and the computed values of f are always unimodal when separated by at least eps * |x| + (tol/3), then x_0 approximates the abscissa of the global minimum of f on the interval lower,upper with an error less than eps * |x_0|+ tol.<br> If f is not unimodal, then optimize() may approximate a local, but perhaps non-global, minimum to the same accuracy.
功能f永远不会计算在两点紧密联系起来比eps * |x_0| + (tol/3)eps约sqrt(.Machine$double.eps)和x_0是最后的横坐标如果optimize()$minimum是一个单峰函数和计算值f总是单峰时，至少feps *分开，然后 |x| + (tol/3)参考。 x_0接近全球最低f在区间lower,upper错误少于eps * |x_0|+ tol参考。如果f横坐标不是单式，然后optimize()5月近似的地方，但也许非全局，以相同的精度最低。
The first evaluation of f is always at x_1 = a + (1-φ)(b-a) where (a,b) = (lower, upper) and phi = (sqrt(5) - 1)/2 = 0.61803.. is the golden section ratio. Almost always, the second evaluation is at x_2 = a + phi(b-a). Note that a local minimum inside [x_1,x_2] will be found as solution, even when f is constant in there, see the last example.
f评价始终是在x_1 = a + (1-φ)(b-a)(a,b) = (lower, upper)和phi = (sqrt(5) - 1)/2 = 0.61803..是黄金分割比例。几乎总是，第二次评估是在x_2 = a + phi(b-a)。请注意，里面[x_1,x_2]当地最低将发现解决方案，即使f是固定在那里，看到的最后一个例子。
f will be called as f(<VAR>x</VAR>, ...) for a numeric value of <VAR>x</VAR>.
ff(<VAR>x</VAR>, ...)的<VAR>的数值将被称为X </变更>。
值----------Value----------
A list with components minimum (or maximum) and objective which give the location of the minimum (or maximum) and the value of the function at that point.
与组件列表minimum（maximum）objective给最低的位置（或最大），并在该点的函数值。
源----------Source----------
A C translation of Fortran code http://www.netlib.org/fmm/fmin.f based on the Algol 60 procedure localmin given in the reference.
Fortran代码http://www.netlib.org/fmm/fmin.f交流基础上的Algol 60程序翻译localmin参考。
参考文献----------References----------
Algorithms for Minimization without Derivatives. Englewood Cliffs N.J.: Prentice-Hall.
参见----------See Also----------
nlm, uniroot.
nlm，uniroot。
举例----------Examples----------
require(graphics)
f <- function (x,a) (x-a)^2
xmin <- optimize(f, c(0, 1), tol = 0.0001, a = 1/3)
xmin
## See where the function is evaluated:[＃见的功能进行评估：]
optimize(function(x) x^2*(print(x)-1), lower=0, upper=10)
## "wrong" solution with unlucky interval and piecewise constant f():[＃“错误”的解决方案，倒霉的间隔和分段常数F（）：]
f <- function(x) ifelse(x > -1, ifelse(x < 4, exp(-1/abs(x - 1)), 10), 10)
fp <- function(x) { print(x); f(x) }
plot(f, -2,5, ylim = 0:1, col = 2)
optimize(fp, c(-4, 20))# doesn't see the minimum[没有看到的最低]
optimize(fp, c(-7, 20))# ok[确定]
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-2-16 17:27:34
