zoukankan      html  css  js  c++  java
  • matlab小记(四)

    在matlab中如何给目标函数传送独立的变量参数。

    Passing Extra Parameters

     

    Extra Parameters, Fixed Variables, or Data

    Sometimes objective or constraint functions have parameters in addition to the independent variable. The extra parameters can be data, or can represent variables that do not change during the optimization. There are three methods of passing these parameters:

    Global variables are troublesome because they do not allow names to be reused among functions. It is better to use one of the other two methods.

    For example, suppose you want to minimize the function

     
    f(x)=(abx21+x41/3)x21+x1x2+(c+cx22)x22 (2-2)

    for different values of ab, and c. Solvers accept objective functions that depend only on a single variable (x in this case). The following sections show how to provide the additional parameters ab, and c. The solutions are for parameter values a = 4, b = 2.1, and c = 4 near x0 = [0.5 0.5] using fminunc.

     

    Anonymous Functions

    To pass parameters using anonymous functions:

    1. Write a file containing the following code:

      function y = parameterfun(x,a,b,c)
      y = (a - b*x(1)^2 + x(1)^4/3)*x(1)^2 + x(1)*x(2) + ...
          (-c + c*x(2)^2)*x(2)^2;
    2. Assign values to the parameters and define a function handle f to an anonymous function by entering the following commands at the MATLAB® prompt:

      a = 4; b = 2.1; c = 4; % Assign parameter values
      x0 = [0.5,0.5];
      f = @(x)parameterfun(x,a,b,c);
    3. Call the solver fminunc with the anonymous function:

      [x,fval] = fminunc(f,x0)

      The following output is displayed in the command window:

      Local minimum found.
      
      Optimization completed because the size of the gradient is less than
      the default value of the function tolerance.
      
      x =
         -0.0898    0.7127
      
      fval =
         -1.0316
     

    Note:   The parameters passed in the anonymous function are those that exist at the time the anonymous function is created. Consider the example

    a = 4; b = 2.1; c = 4;
    f = @(x)parameterfun(x,a,b,c)

    Suppose you subsequently change, a to 3 and run

    [x,fval] = fminunc(f,x0)

    You get the same answer as before, since parameterfun uses a = 4, the value when f was created.

    To change the parameters that are passed to the function, renew the anonymous function by reentering it:

    a = 3;
    f = @(x)parameterfun(x,a,b,c)

    You can create anonymous functions of more than one argument. For example, to use lsqcurvefit, first create a function that takes two input arguments, x and xdata:

    fh = @(x,xdata)(sin(x).*xdata +(x.^2).*cos(xdata));
    x = pi; xdata = pi*[4;2;3];
    fh(x, xdata)
    
    ans =
    
        9.8696
        9.8696
       -9.8696

    Now call lsqcurvefit:

    % Assume ydata exists
    x = lsqcurvefit(fh,x,xdata,ydata)
     

    Nested Functions

    To pass the parameters for Equation 2-2 via a nested function, write a single file that

    • Accepts abc, and x0 as inputs

    • Contains the objective function as a nested function

    • Calls fminunc

    Here is the code for the function file for this example:

    function [x,fval] =  runnested(a,b,c,x0) 
    [x,fval] = fminunc(@nestedfun,x0);
    % Nested function that computes the objective function     
        function y = nestedfun(x)
            y = (a - b*x(1)^2 + x(1)^4/3)*x(1)^2 + x(1)*x(2) +...
                (-c + c*x(2)^2)*x(2)^2;     
        end
    end

    The objective function is the nested function nestedfun, which has access to the variables ab, and c.

    To run the optimization, enter:

    a = 4; b = 2.1; c = 4;% Assign parameter values
    x0 = [0.5,0.5];
    [x,fval] = runnested(a,b,c,x0)
    

    The output is the same as in Anonymous Functions.

     

    Global Variables

    Global variables can be troublesome, so it is better to avoid using them. To use global variables, declare the variables to be global in the workspace and in the functions that use the variables.

    1. Write a function file:

      function y = globalfun(x)
      global a b c
      y = (a - b*x(1)^2 + x(1)^4/3)*x(1)^2 + x(1)*x(2) + ...
          (-c + c*x(2)^2)*x(2)^2;
    2. In your MATLAB workspace, define the variables and run fminunc:

      global a b c;
      a = 4; b = 2.1; c = 4; % Assign parameter values
      x0 = [0.5,0.5];
      [x,fval] = fminunc(@globalfun,x0)

    The output is the same as in Anonymous Functions.

    参考资料:matlab帮助文档

  • 相关阅读:
    Redis为什么使用单进程单线程方式也这么快
    高性能IO模型浅析
    计算机基础知识_硬件知识
    计算机基础知识_进制转化
    计算机基础知识
    C语言_第一讲_C语言入门
    C语言_第二讲_规范以及常用数据类型
    COM_第四讲_保存GUID_优化使用代码
    4.性能下降原因和常见的Join查询
    3.MySQL的架构介绍
  • 原文地址:https://www.cnblogs.com/Qiangcm/p/8794247.html
Copyright © 2011-2022 走看看