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  • [Octave] fminunc()

    fminunc( FCN, X0);
    
    fminunc( FCN, C0, Options);
    
    [X, FVEC, INFO, OUTPUT, GRAD, HESS] = fminunc (FCN, ...);
    
    %Solve an unconstrained optimization problem defined by the function FCN.
    
    %X0 determines a starting guess.
    
    %OPTIONS is a structure specifying additional options.  Currently, `fminunc' recognizes these %options: "FunValCheck", "OutputFcn", "TolX", "TolFun", "MaxIter", "MaxFunEvals", "GradObj", %"FinDiffType", "TypicalX", "AutoScaling".  (optimset)
    
    %On return, FVAL contains the value of the function FCN evaluated at X
    
    %INFO may be one of the following values:
    %1 Converged to a solution point.
    %2 Last relative step size was less that TolX.
    %3 Last relative decrease in function value was less than TolF.
    %0 Iteration limit exceeded.
    %-3 The trust region radius became excessively small.
    
    %Notes: If you only have a single nonlinear equation of one
    %     variable then using `fminbnd' is usually a much better idea.  The
    %     algorithm used is a gradient search which depends on the objective
    %     function being differentiable.  If the function has
    %     discontinuities it may be better to use a derivative-free
    %     algorithm such as `fminsearch'.
    

      

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  • 原文地址:https://www.cnblogs.com/KennyRom/p/6528230.html
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