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  • Fminunc函数和Optimset函数

    万次阅读 多人点赞 2017-08-03 12:02:58
    Optimset函数:‘Gradobj’指用户自定义的目标函数梯度;‘MaxITer’指最大迭代次数,‘100’也就是最大迭代次数,这一项只能为整数。Fminunc函数:有三个输入,第一个输入为costfunction函数的句柄

    这里写图片描述

    costFunction函数是自定义函数;输入是theta,输出是jVal和gradient,其中jVal是对照左边求损失函数的,gradient是对照左边求损失函数的偏导。

    Optimset函数:‘Gradobj’指用户自定义的目标函数梯度;‘MaxITer’指最大迭代次数,‘100’也就是最大迭代次数,这一项只能为整数。

    Fminunc函数:有三个输入,第一个输入为costfunction函数的句柄,第二个输入为设置的初始theta值,第三个输入为optimset函数的返回值。
    有三个输出,optTheta为经函数计算得出的theta值,也就是损失函数最小时theta的取值,以上图为例,令损失函数取最小值的theta值都是5,下面会验证。exitFlagexitflag返回值为0或1,表示在theta点定义的损失函数是否收敛,值为1表示收敛。functionVal为costFunction函数中jVal的值。
    验证

    展开全文
  • function [jVal, gradient]=costFunction(theta) jVal = (theta(1)-5)^2 + (theta(2)-5)^2; gradient = zeros(2, 1); gradient(1) = 2*(theta(1)-5); gradient(2) = 2*(theta(2)-5); end
  • >> opt=optimset opt = Display: [] MaxFunEvals: [] MaxIter: [] TolFun: [] TolX: [] FunValCheck: [] OutputFcn: [] PlotFcns: [] ActiveConstrTol: [] Algorithm: [] AlwaysHonorConstraints: [] BranchStrategy...

    >> opt=optimset

    opt =

    Display: []

    MaxFunEvals: []

    MaxIter: []

    TolFun: []

    TolX: []

    FunValCheck: []

    OutputFcn: []

    PlotFcns: []

    ActiveConstrTol: []

    Algorithm: []

    AlwaysHonorConstraints: []

    BranchStrategy: []

    DerivativeCheck: []

    Diagnostics: []

    DiffMaxChange: []

    DiffMinChange: []

    FinDiffRelStep: []

    FinDiffType: []

    GoalsExactAchieve: []

    GradConstr: []

    GradObj: []

    HessFcn: []

    Hessian: []

    HessMult: []

    HessPattern: []

    HessUpdate: []

    InitialHessType: []

    InitialHessMatrix: []

    InitBarrierParam: []

    InitTrustRegionRadius: []

    Jacobian: []

    JacobMult: []

    JacobPattern: []

    LargeScale: []

    LineSearchType: []

    MaxNodes: []

    MaxPCGIter: []

    MaxProjCGIter: []

    MaxRLPIter: []

    MaxSQPIter: []

    MaxTime: []

    MeritFunction: []

    MinAbsMax: []

    NodeDisplayInterval: []

    NodeSearchStrategy: []

    NoStopIfFlatInfeas: []

    ObjectiveLimit: []

    PhaseOneTotalScaling: []

    Preconditioner: []

    PrecondBandWidth: []

    RelLineSrchBnd: []

    RelLineSrchBndDuration: []

    ScaleProblem: []

    Simplex: []

    SubproblemAlgorithm: []

    TolCon: []

    TolConSQP: []

    TolGradCon: []

    TolPCG: []

    TolProjCG: []

    TolProjCGAbs: []

    TolRLPFun: []

    TolXInteger: []

    TypicalX: []

    UseParallel: []

    展开全文
  • 利用optimset函数,可以创建和编辑参数结构;利用optimget函数,可以获得options优化参数。●optimget函数功能:获得options优化参数。语法:val = optimget(options,'param')val = optimget(options,'param',...

    利用optimset函数,可以创建和编辑参数结构;利用optimget函数,可以获得options优化参数。

    ●optimget函数

    功能:获得options优化参数。

    语法:

    val = optimget(options,'param')

    val = optimget(options,'param',default)

    描述:

    val = optimget(options,'param') 返回优化参数options中指定的参数的值。只需要用参数开头的字母来定义参数就行了。

    val = optimget(options,'param',default) 若options结构参数中没有定义指定参数,则返回缺省值。注意,这种形式的函数主要用于其它优化函数。

    举例:

    1.下面的命令行将显示优化参数options返回到my_options结构中:

    val = optimget(my_options,'Display')

    2.下面的命令行返回显示优化参数options到my_options结构中(就象前面的例子一样),但如果显示参数没有定义,则返回值'final':

    optnew = optimget(my_options,'Display','final');

    ●optimset函数

    功能:创建或编辑优化选项参数结构。

    语法:

    options = optimset('param1',value1,'param2',value2,...)

    optimset

    options = optimset

    options = optimset(optimfun)

    options = optimset(oldopts,'param1',value1,...)

    options = optimset(oldopts,newopts)

    描述:

    options = optimset('param1',value1,'param2',value2,...) 创建一个称为options的优化选项参数,其中指定的参数具有指定值。所有未指定的参数都设置为空矩阵[](将参数设置为[]表示当options传递给优化函数时给参数赋缺省值)。赋值时只要输入参数前面的字母就行了。

    optimset函数没有输入输出变量时,将显示一张完整的带有有效值的参数列表。

    options = optimset (with no input arguments) 创建一个选项结构options,其中所有的元素被设置为[]。

    options = optimset(optimfun) 创建一个含有所有参数名和与优化函数optimfun相关的缺省值的选项结构options。

    options = optimset(oldopts,'param1',value1,...) 创建一个oldopts的拷贝,用指定的数值修改参数。

    options = optimset(oldopts,newopts) 将已经存在的选项结构oldopts与新的选项结构newopts进行合并。newopts参数中的所有元素将覆盖oldopts参数中的所有对应元素。

    举例:

    1.下面的语句创建一个称为options的优化选项结构,其中显示参数设为'iter',TolFun参数设置为1e-8:

    options = optimset('Display','iter','TolFun',1e-8)

    2.下面的语句创建一个称为options的优化结构的拷贝,改变TolX参数的值,将新值保存到optnew参数中:

    optnew = optimset(options,'TolX',1e-4);

    3.下面的语句返回options优化结构,其中包含所有的参数名和与fminbnd函数相关的缺省值:

    options = optimset('fminbnd')

    4.若只希望看到fminbnd函数的缺省值,只需要简单地键入下面的语句就行了:

    optimset fminbnd

    或者输入下面的命令,其效果与上面的相同:

    optimset('fminbnd')

    展开全文
  • 在练习2中使用了到了两个函数optimset和fminunc。 % Set options for fminunc options = optimset('GradObj', 'on', 'MaxIter', 400); %set the GradObj option to on,which tells fminunc that our function %...

    在练习2中使用了到了两个函数:optimset和fminunc。

    %  Set options for fminunc
    options = optimset('GradObj', 'on', 'MaxIter', 400);
    %set the GradObj option to on,which tells fminunc that our function
    %returns both the cost and gradient.
    %This allows fminunc  to use the gradient when minimizing the function.
    %set the MaxIter option to 400, so that fminunc will run for at most 400
    %steps before it terminates.
    
    %  Run fminunc to obtain the optimal theta
    %  This function will return theta and the cost 
    [theta, cost] = ...
    	fminunc(@(t)(costFunction(t, X, y)), initial_theta, options);
    

    1、通过help查看optimset函数

    >> help optimset
     optimset Create/alter optimization OPTIONS structure.
        OPTIONS = optimset('PARAM1',VALUE1,'PARAM2',VALUE2,...) creates an
        optimization options structure OPTIONS in which the named parameters have
        the specified values.  Any unspecified parameters are set to [] (parameters
        with value [] indicate to use the default value for that parameter when
        OPTIONS is passed to the optimization function). It is sufficient to type
        only the leading characters that uniquely identify the parameter.  Case is
        ignored for parameter names.
        NOTE: For values that are strings, the complete string is required.
     
        OPTIONS = optimset(OLDOPTS,'PARAM1',VALUE1,...) creates a copy of OLDOPTS
        with the named parameters altered with the specified values.
     
        OPTIONS = optimset(OLDOPTS,NEWOPTS) combines an existing options structure
        OLDOPTS with a new options structure NEWOPTS.  Any parameters in NEWOPTS
        with non-empty values overwrite the corresponding old parameters in
        OLDOPTS.
     
        optimset with no input arguments and no output arguments displays all
        parameter names and their possible values, with defaults shown in {}
        when the default is the same for all functions that use that parameter. 
        Use optimset(OPTIMFUNCTION) to see parameters for a specific function.
     
        OPTIONS = optimset (with no input arguments) creates an options structure
        OPTIONS where all the fields are set to [].
     
        OPTIONS = optimset(OPTIMFUNCTION) creates an options structure with all
        the parameter names and default values relevant to the optimization
        function named in OPTIMFUNCTION. For example,
                optimset('fminbnd')
        or
                optimset(@fminbnd)
        returns an options structure containing all the parameter names and
        default values relevant to the function 'fminbnd'.
     
     optimset PARAMETERS for MATLAB
     Display - Level of display [ off | iter | notify | final ]
     MaxFunEvals - Maximum number of function evaluations allowed
                          [ positive integer ]
     MaxIter - Maximum number of iterations allowed [ positive scalar ]
     TolFun - Termination tolerance on the function value [ positive scalar ]
     TolX - Termination tolerance on X [ positive scalar ]
     FunValCheck - Check for invalid values, such as NaN or complex, from 
                   user-supplied functions [ {off} | on ]
     OutputFcn - Name(s) of output function [ {[]} | function ] 
               All output functions are called by the solver after each
               iteration.
     PlotFcns - Name(s) of plot function [ {[]} | function ]
               Function(s) used to plot various quantities in every iteration
     
      Note to Optimization Toolbox users:
      To see the parameters for a specific function, check the documentation page 
      for that function. For instance, enter
        doc fmincon
      to open the reference page for fmincon.
     
      You can also see the options in the Optimization Tool. Enter
        optimtool
               
        Examples:
          To create an options structure with the default parameters for FZERO
            options = optimset('fzero');
          To create an options structure with TolFun equal to 1e-3
            options = optimset('TolFun',1e-3);
          To change the Display value of options to 'iter'
            options = optimset(options,'Display','iter');
     
        See also optimget, fzero, fminbnd, fminsearch, lsqnonneg.
    
        Reference page for optimset
        Other functions named optimset
    

    optimset函数创建或改变最优化OPTIONS结构。
    OPTIONS = optimset(‘PARAM1’,VALUE1,‘PARAM2’,VALUE2,…) 创建了一个优化选项结构OPTIONS,当OPTIONS传递优化参数时,将已经命名的参数赋值为特殊值,未有赋值的参数设置为[],即设置为默认值。
    OPTIONS = optimset ()会创建一个优选结构OPTIONS其所有的参数设置为[].
    OPTIONS = optimset(OPTIMFUNCTION)会创建一个针对优化函数OPTIMFUNCTION的优选结构OPTIONS,其参数为优化函数默认值。
    比如说,可以使用语句optimset(‘fminbnd’)或者optimset(@fminbnd),其会返回针对优化函数 'fminbnd’的优选结构,其参数均为优化函数 'fminbnd’默认值。
    通过上述分析,可以使用命令optimset(‘fminunc’)查看函数的参数。

    >> optimset('fminunc')
    
    ans = 
    
                       Display: 'final'
                   MaxFunEvals: '100*numberofvariables'
                       MaxIter: 400
                        TolFun: 1.0000e-06
                          TolX: 1.0000e-06
                   FunValCheck: 'off'
                     OutputFcn: []
                      PlotFcns: []
               ActiveConstrTol: []
                     Algorithm: []
        AlwaysHonorConstraints: []
               DerivativeCheck: 'off'
                   Diagnostics: 'off'
                 DiffMaxChange: Inf
                 DiffMinChange: 0
                FinDiffRelStep: []
                   FinDiffType: 'forward'
             GoalsExactAchieve: []
                    GradConstr: []
                       GradObj: 'off'
                       HessFcn: []
                       Hessian: 'off'
                      HessMult: []
                   HessPattern: 'sparse(ones(numberofvariables))'
                    HessUpdate: 'bfgs'
               InitialHessType: 'scaled-identity'
             InitialHessMatrix: []
              InitBarrierParam: []
         InitTrustRegionRadius: []
                      Jacobian: []
                     JacobMult: []
                  JacobPattern: []
                    LargeScale: 'on'
                      MaxNodes: []
                    MaxPCGIter: 'max(1,floor(numberofvariables/2))'
                 MaxProjCGIter: []
                    MaxSQPIter: []
                       MaxTime: []
                 MeritFunction: []
                     MinAbsMax: []
            NoStopIfFlatInfeas: []
                ObjectiveLimit: -1.0000e+20
          PhaseOneTotalScaling: []
                Preconditioner: []
              PrecondBandWidth: 0
                RelLineSrchBnd: []
        RelLineSrchBndDuration: []
                  ScaleProblem: []
                       Simplex: []
           SubproblemAlgorithm: []
                        TolCon: []
                     TolConSQP: []
                    TolGradCon: []
                        TolPCG: 0.1000
                     TolProjCG: []
                  TolProjCGAbs: []
                      TypicalX: 'ones(numberofvariables,1)'
                   UseParallel: 0 
    

    故函数options = optimset(‘GradObj’, ‘on’, ‘MaxIter’, 400)为了一个创建名称为options优选结构,其将参数’GradObj’设置为’On’,使用用户创建的梯度函数,将’MaxIter’设置为400,最大迭代次数为400。
    2、关于语句[theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options)
    使用help fminunc查看fminunc函数的使用方法。

    >> help fminunc
     fminunc finds a local minimum of a function of several variables.
        X = fminunc(FUN,X0) starts at X0 and attempts to find a local minimizer
        X of the function FUN. FUN accepts input X and returns a scalar
        function value F evaluated at X. X0 can be a scalar, vector or matrix. 
     
        X = fminunc(FUN,X0,OPTIONS) minimizes with the default optimization
        parameters replaced by values in OPTIONS, an argument created with the
        OPTIMOPTIONS function.  See OPTIMOPTIONS for details. Use the
        SpecifyObjectiveGradient option to specify that FUN also returns a
        second output argument G that is the partial derivatives of the
        function df/dX, at the point X. Use the HessianFcn option to specify
        that FUN also returns a third output argument H that is the 2nd partial
        derivatives of the function (the Hessian) at the point X. The Hessian
        is only used by the trust-region algorithm.
     
        X = fminunc(PROBLEM) finds the minimum for PROBLEM. PROBLEM is a
        structure with the function FUN in PROBLEM.objective, the start point
        in PROBLEM.x0, the options structure in PROBLEM.options, and solver
        name 'fminunc' in PROBLEM.solver. Use this syntax to solve at the 
        command line a problem exported from OPTIMTOOL. 
     
        [X,FVAL] = fminunc(FUN,X0,...) returns the value of the objective 
        function FUN at the solution X.
     
        [X,FVAL,EXITFLAG] = fminunc(FUN,X0,...) returns an EXITFLAG that
        describes the exit condition. Possible values of EXITFLAG and the
        corresponding exit conditions are listed below. See the documentation
        for a complete description.
     
          1  Magnitude of gradient small enough. 
          2  Change in X too small.
          3  Change in objective function too small.
          5  Cannot decrease function along search direction.
          0  Too many function evaluations or iterations.
         -1  Stopped by output/plot function.
         -3  Problem seems unbounded. 
        
        [X,FVAL,EXITFLAG,OUTPUT] = fminunc(FUN,X0,...) returns a structure 
        OUTPUT with the number of iterations taken in OUTPUT.iterations, the 
        number of function evaluations in OUTPUT.funcCount, the algorithm used 
        in OUTPUT.algorithm, the number of CG iterations (if used) in
        OUTPUT.cgiterations, the first-order optimality (if used) in
        OUTPUT.firstorderopt, and the exit message in OUTPUT.message.
     
        [X,FVAL,EXITFLAG,OUTPUT,GRAD] = fminunc(FUN,X0,...) returns the value 
        of the gradient of FUN at the solution X.
     
        [X,FVAL,EXITFLAG,OUTPUT,GRAD,HESSIAN] = fminunc(FUN,X0,...) returns the 
        value of the Hessian of the objective function FUN at the solution X.
     
        Examples
          FUN can be specified using @:
             X = fminunc(@myfun,2)
     
        where myfun is a MATLAB function such as:
     
            function F = myfun(x)
            F = sin(x) + 3;
     
          To minimize this function with the gradient provided, modify
          the function myfun so the gradient is the second output argument:
             function [f,g] = myfun(x)
              f = sin(x) + 3;
              g = cos(x);
          and indicate the gradient value is available by creating options with
          OPTIONS.SpecifyObjectiveGradient set to true (using OPTIMOPTIONS):
             options = optimoptions('fminunc','SpecifyObjectiveGradient',true);
             x = fminunc(@myfun,4,options);
     
          FUN can also be an anonymous function:
             x = fminunc(@(x) 5*x(1)^2 + x(2)^2,[5;1])
     
        If FUN is parameterized, you can use anonymous functions to capture the
        problem-dependent parameters. Suppose you want to minimize the 
        objective given in the function myfun, which is parameterized by its 
        second argument c. Here myfun is a MATLAB file function such as
     
          function [f,g] = myfun(x,c)
     
          f = c*x(1)^2 + 2*x(1)*x(2) + x(2)^2; % function
          g = [2*c*x(1) + 2*x(2)               % gradient
               2*x(1) + 2*x(2)];
     
        To optimize for a specific value of c, first assign the value to c. 
        Then create a one-argument anonymous function that captures that value 
        of c and calls myfun with two arguments. Finally, pass this anonymous 
        function to fminunc:
     
          c = 3;                              % define parameter first
          options = optimoptions('fminunc','SpecifyObjectiveGradient',true); % indicate gradient is provided 
          x = fminunc(@(x) myfun(x,c),[1;1],options)
     
        See also optimoptions, fminsearch, fminbnd, fmincon, @, inline.
    
        Reference page for fminunc
    
    >> 
    

    fminunc是一个寻找局部最小值函数。
    X = fminunc(FUN,X0) 表示从X0开始并尝试通过函数FUN寻找局部最小值. FUN接受输入参数X并返回一个在点X计算出来的标量,X0可以是标量、矢量和矩阵。
    X = fminunc(FUN,X0,OPTIONS)使用了优化参数OPTIONS而不是默认参数。
    例如FUN可以是匿名函数:
    X = fminunc(@myfun,2)
    function F = myfun(x)
    F = sin(x) + 3;
    为了使用提供的梯度函数,修改myfun函数,增加其输出第二参数为梯度。并增加优选结构options。
    function [f,g] = myfun(x)
    f = sin(x) + 3;
    g = cos(x);
    options = optimoptions(‘fminunc’,‘SpecifyObjectiveGradient’,true);
    x = fminunc(@myfun,4,options);

    综上,理解语句[theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options)的意思为:
    (1)@(t)(costFunction(t, X, y)为匿名函数,原本函数costFunction为输入三个参数theta,X,y输出两个参数J,gradient,通过匿名函数后,其输入参数变成一个t,另外两个参数X,y变成固定值(X,y前面已经定义),故该表达符合fminunc函数的第一个输入参数,即输入为函数,且该函数仅有一个输入,输出第一个值为计算的最小值,输出第二个值为用户自定义梯度计算值;
    (2)initial_theta为寻找局部最小值的起始位置;
    (3)options为函数fminunc的优化配置,即最大迭代次数为400,用户梯度函数设置为On;
    (4)根据解释[X,FVAL] = fminunc(FUN,X0,…) returns the value of the objective function FUN at the solution X。
    [theta, cost] = fminunc(@(t)(costFunction(t, X, y)), initial_theta, options)表示的是返回函数costFunction的局部最小值,其在点theta处,函数costFunction在theta处值为cost。

    展开全文
  • 非线性优化-matlab函数库-optimset 2010-03-24 10:20 创建或编辑一个最优化参数选项 句法规则 options = optimset'param1,value1'param2,value2) %设置所有参数及其值未设置的为默认值 options = optimset(optimfun?...
  • options = optimset('param1',value1,'param2',value2,...) %指定参数param拥有指定的值,任何未指定值的参数都将设置为[](表示将options传递给优化函数的时候使用 %该参数的默认值),参数名称忽略大小写。 ...
  • 非线性优化-matlab函数库-optimset

    万次阅读 多人点赞 2014-05-06 23:20:14
    创建或编辑一个最优化参数选项 句法规则 options = optimset('param1',value1,'param2',value2,...) %设置所有参数及其值,未设置...options = optimset(optimfun) %设置与最优化函数有关的参数为默认 options
  • 创建或编辑一个最优化参数选项句法规则options = optimset('param1',value1,'param2',value2,...) %设置所有参数及其值,未设置的为默认值options = optimset(optimfun) %设置与最优化函数有关的参数为默认options =...
  • matlab fminsearch函数 …

    万次阅读 2017-03-14 10:55:48
    fminsearch能够从一个初始值开始,找到一个标量函数的最小值。通常被称为无约束非线性优化  x = fminsearch(fun,x0) ...以优化参数指定的结构最小化函数,可以用optimset函数定义这些参数。(见mat
  • matlab:optimset的用法

    2021-08-24 17:35:03
    optimset 创建或修改优化选项结构 syntax options = optimset(Name,Value) optimset options = optimset options = optimset(optimfun) options = optimset(oldopts,Name,Value) options = optimset(oldopts,newopts...
  • Choose Between optimoptions and optimset 在optimoptions和optimset之间进行选择 原文链接:https://ww2.mathworks.cn/help/optim/ug/optimoptions-and-optimset.html 可使用网页自带的微软翻译进行翻译,加粗表示...
  • [Octave] optimset()

    2019-09-28 03:47:37
    Create options struct for optimization functions. optimset('parameter', value, ...); %设置所有参数及其值,未设置的为...%设置与最优化函数有关的参数为默认 optimset(old, 'parmeter', 'value', ....
  • 转发:Octave] optimset()

    2020-02-27 16:47:35
    optimset('parameter', value, ...);...%设置与最优化函数有关的参数为默认 optimset(old, 'parmeter', 'value', ... ); %复制已有的设置,并修改特定项 optimset(old, new); %两项合并 转自:https://blog...
  • MATLAB自学笔记 (八)

    2020-03-26 01:22:37
    MATLAB自学笔记 八MATLAB优化工具箱简介优化工具箱函数最小化函数方程求解函数最小二乘函数(曲线拟合函数)函数参数设置optimget函数optimset函数 MATLAB优化工具箱简介 优化工具箱函数 最小化函数 函数 描述 ...
  • 必须参数funx1x2 说明 fun 为目标函数用M文件或Inline定义 x1x2为目标函数的自变量的取值范围 options是一个结构型的变量用于指定优化参数可通过optimset函数设置 help optimset;例子1求解;例子2求解模型;方法1;2...
  • optimset函数 功能:创建或编辑优化选项参数结构。 语法: 1 options = optimset(‘param1’,value1,’param2’,value2,…) 2 options = optimset 3 options = optimset(oldopts,’param1’,value1,…) ...
  • MATLAB 中sim函数具体使用方法sim函数的变量[t,x,y]=sim(f1,tspan,options,ut)其中f1为SIMULINK的模型名,tspan为仿真时间控制变量;参数options为模型控制参数;ut为外部输入向量。ut在我这个题目中是什么?options...
  • 不使用导数的优化fminsearch 函数在无约束下求问题的最小值。它使用的算法不估计目标函数的任何导数。它使用 fminsearch Algorithm 中所述的几何搜索方法。使用 fminsearch 最小化香蕉函数...options = optimset('Ou...
  • Optimset函数:‘Gradobj’指用户自定义的目标函数梯度;‘MaxITer’指最大迭代次数,‘100’也就是最大迭代次数,这一项只能为整数。 Fminunc函数:有三个输入,第一个输入为costfunction函...
  • Fsolve函数是一种参数优化,其调用optimset函数,其中的display 表示不显示迭代的参数 求根符号函数solve,求取的x是一个符号解,需要eval函数转换成数值解 三个小数点,是续航符,表示内容紧接 在命令行窗口输入cd...
  • 点击蓝字关注我们本文主要分两部分:第一部分介绍matlab中非线性方程求解,第二部分将介绍如何用matlab去求解函数的极值。+一、非线性方程数值求解1、单变量非线性方程求解函数的调用格式为:x= fzero(filename,x0)...
  • fminunc函数

    千次阅读 2019-03-01 15:02:11
    options = optimset('GradObj', 'on', 'MaxIter', '100'); initialTheta = zeros(2,1); [optTheta, functionVal, exitFlag] = fminunc(@costFunction, initialTheta, options); fminunc表示Octave里无约束最小化...
  • [quote][parse]Bush wrote:[/parse]用Matlab软件:知道指数函数y=exp(a*x+b)以及该函数的一些点,即,并求出a,b的值????????那位大侠知道,请速发:usa_hp@163.com不尽感谢!!!!!!!!!!!!!!![/...
  • Matlab常用函数

    千次阅读 2014-06-03 10:15:00
    Matlab有没有求矩阵行数/列数/维数的函数? ndims(A)返回A的维数 size(A)返回A各个维的最大元素个数 length(A)返回max(size(A)) [m,n]=size(A)如果A是二维数组,返回行数和列数 nnz(A)返回A中非0元素的个数 ...
  • MATLAB求函数零点—fzero函数

    万次阅读 2014-03-06 20:31:16
    MATLAB 函数的零点 作者:未知 信息来源:未知 2006-1-26 字体大小:小 中 大网友评论条 进入论坛  6 函数的零点5。2 一元函数的零点5。2 任意一元函数零点的精确解【 * 例 5。2-1 】通过求 的零点,...

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