>> help fminunc

 FMINUNC  Finds the minimum of a function of several variables.
    X=FMINUNC(FUN,X0) starts at X0 and finds a minimum 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 the structure OPTIONS, an argument
    created with the OPTIMSET function.  See OPTIMSET for details.  Used
    options are Display, TolX, TolFun, DerivativeCheck, Diagnostics, GradObj,
    HessPattern, LineSearchType, Hessian, HessMult, HessUpdate, MaxFunEvals, 
    MaxIter, DiffMinChange and DiffMaxChange, LargeScale, MaxPCGIter, 
    PrecondBandWidth, TolPCG, TypicalX. Use the GradObj 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 Hessian 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 large-scale method, not the 
    line-search method. 
 
    X=FMINUNC(FUN,X0,OPTIONS,P1,P2,...) passes the problem-dependent 
    parameters P1,P2,... directly to the function FUN, e.g. FUN would be
    called using feval as in: feval(FUN,X,P1,P2,...).  
    Pass an empty matrix for OPTIONS to use the default values.
 
    [X,FVAL]=FMINUNC(FUN,X0,...) returns the value of the objective 
    function FUN at the solution X.
 
    [X,FVAL,EXITFLAG]=FMINUNC(FUN,X0,...) returns a string EXITFLAG that 
    describes the exit condition of FMINUNC.  
    If EXITFLAG is:
       > 0 then FMINUNC converged to a solution X.
       0   then the maximum number of function evaluations was reached.
       < 0 then FMINUNC did not converge to a solution.
    
    [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, and the first-order optimality (if used) in 
    OUTPUT.firstorderopt.
 
    [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 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 an options
      structure with OPTIONS.GradObj set to 'on' (using OPTIMSET):
         options = optimset('GradObj','on');
         x = fminunc('myfun',4,options);
 
      FUN can also be an inline object:
         x = fminunc(inline('sin(x)+3'),4);
 
    See also OPTIMSET, FMINSEARCH, FMINBND, FMINCON, @, INLINE.