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FindMinimum

Usage

FindMinimum[f,  x,   ] 从点 x= 搜索 f 的局部最优解。


Notes

FindMinimum 返回  ,  x->   形式的列表,其中 是找到的f 的最小值,且  是要找的 x的值。
FindMinimum[f,  x,   ,    ]   搜索 f 的局部最优解。如果不能找到 f 的符号导数,必须使用这个形式。
FindMinimum[f,  x, xstart, xmin, xmax ]搜索局部最优解,如果x 超出了xminxmax的范围,停止搜索。
FindMinimum[f,  x,   ,  y,   , ... ] 在一个多变量的函数中搜索局部最优解。
FindMinimum 有属性 HoldAll.
FindMinimum 从它达到的每个点的最速下降路径进行计算。它找到的最小值是局部的最小值,但未必是全局的。
• 可以给出下面的选项:
"\!\(\*StyleBox[\"\\\"AccuracyGoal\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"Automatic\\\"\", \"MR\"]\) "精确搜索函数最小值
"\!\(\*StyleBox[\"\\\"Compiled\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"True\\\"\", \"MR\"]\) "是否函数应当被编译
"\!\(\*StyleBox[\"\\\"Gradient\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"Automatic\\\"\", \"MR\"]\) "梯度函数列表{"\!\(\*StyleBox[\"\\\"D[\\\"\", \"MR\"]\)\!\(\*StyleBox[\"\\\"f\\\"\", \"TI\"]\)\!\(\*StyleBox[\"\\\",\\\"\", \"MR\"]\) \!\(\*StyleBox[\"\\\"x\\\"\", \"TI\"]\)\!\(\*StyleBox[\"\\\"],\\\"\", \"MR\"]\) \!\(\*StyleBox[\"\\\"D[\\\"\", \"MR\"]\)\!\(\*StyleBox[\"\\\"f\\\"\", \"TI\"]\)\!\(\*StyleBox[\"\\\",\\\"\", \"MR\"]\) \!\(\*StyleBox[\"\\\"y\\\"\", \"TI\"]\)\!\(\*StyleBox[\"\\\"],\\\"\", \"MR\"]\) ... "}" "
"\!\(\*StyleBox[\"\\\"MaxIterations\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"30\\\"\", \"MR\"]\) "使用的最大迭代数
"\!\(\*StyleBox[\"\\\"PrecisionGoal\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"Automatic\\\"\", \"MR\"]\) "搜索函数最小值的精度
"\!\(\*StyleBox[\"\\\"WorkingPrecision\\\"\", \"MR\"]\) ""\!\(\*StyleBox[\"\\\"Precision[1.]\\\"\", \"MR\"]\) "用在内部计算的数字数目
AccuracyGoalPrecisionGoal缺省设置是小于WorkingPrecision10个数位。
FindMinimum 继续直到由AccuracyGoalPrecisionGoal指定的目标中的任何一个被达到。
• 参见Mathematica 全书: 1.6.5节 and 3.9.8节.
• 实现注释: 参见 A.9.4节.
• 同时参见: ConstrainedMin, LinearProgramming, D, Fit.
• 相关的包: Statistics`NonlinearFit`.
Further Examples

This finds the value of x which minimizes  , starting from x = 2.

In[1]:=  

Out[1]=

Here is a function with many local minima.

Evaluate the cell to see the graphic.

In[2]:=  

FindMinimum finds the local minimum closest to x = 1. This is not the global minimum for the function.

In[3]:=  

Out[3]=

This finds the local minimum of a function of two variables. As in FindRoot, it is a good idea to choose starting values that are not too special.

In[4]:=  

Out[4]=

This finds a minimum, explicitly specifying a gradient to use.

In[5]:=  

Out[5]=

Using the default setting, a different local minimum is found.

In[6]:=  

Out[6]=



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