You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: maxlik.m
    path of file: ./repos/malini/Codes/FC_EC_calculation code/DynamicFC/jplv7/optimize
    
    the code of the file until where you have to start completion: function result=maxlik(func,b,info,varargin)
% PURPOSE: minimize a log likelihood function
% ------------------------------------------------------------------
% USAGE:     result = maxlike(func,b,info,varargin)
%        or: result = maxlike(func,b,[],varargin) for default options 
% Where: func    = function to be minimized 
%        b       = parameter vector fed to func       
%        info structure containing optimization options
%        .delta  = Increment in numerical derivs                   [.000001]
%        .hess   = Hessian method: ['dfp'], 'bfgs', 'gn', 'marq', 'sd'
%        .maxit  = Maximium iterations                             [100]
%        .lambda = Minimum eigenvalue of Hessian for Marquardt     [.01]
%        .cond   = Tolerance level for condition of Hessian        [1000]
%        .btol   = Tolerance for convergence of parm vector        [1e-4]
%        .ftol   = Tolerance for convergence of objective function [sqrt(eps)] 
%        .gtol   = Tolerance for convergence of gradient           [sqrt(eps)]
%        .prt    = Printing: 0 = None, 1 = Most, 2 = All           [0]
%       varargin = arguments list passed to func
% ------------------------------------------------------------------
% RETURNS: results = a structure variable with fields:
%           .b     = parameter value at the optimum
%           .hess  = numerical hessian at the optimum
%           .bhist = history of b at each iteration
%           .f     = objective function value at the optimum
%           .g     = gradient at the optimum
%           .dg    = change in gradient
%           .db    = change in b parameters
%           .df    = change in objective function
%           .iter  = # of iterations taken
%           .meth  = 'dfp', 'bfgs', 'gn', 'marq', 'sd' (from input)
%           .time  = time (in seconds) needed to find solution
% ------------------------------------------------------------------
% NOTES: This is really Mike Cliff's minz function converted for usage 
%        with the Econometrics Toolbox.
% calls stepz, numz, hessz functions
% ------------------------------------------------------------------

% written by:
% Mike Cliff,  UNC Finance   mcliff@unc.edu
% CREATED  12/8/98
% UPDATED  1/24/99

% changed for consistency with other
% Econometrics Toolbox optimization routines
% by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jlesage@spatial-econometrics.com
% last modified 4/14/2000

infoz.func = func;

% set defaults
infoz.maxit = 100;
infoz.hess = 'bfgs';
infoz.prt = 0;
infoz.cond = 1000;
infoz.btol = 1e-4;
infoz.gtol = sqrt(eps);
infoz.ftol = sqrt(eps);

    
function result=maxlik(func,b,info,varargin)
% PURPOSE: minimize a log likelihood function
% ------------------------------------------------------------------
% USAGE:     result = maxlike(func,b,info,varargin)
%        or: result = maxlike(func,b,[],varargin) for default options 
% Where: func    = function to be minimized 
%        b       = parameter vector fed to func       
%        info structure containing optimization options
%        .delta  = Increment in numerical derivs                   [.000001]
%        .hess   = Hessian method: ['dfp'], 'bfgs', 'gn', 'marq', 'sd'
%        .maxit  = Maximium iterations                             [100]
%        .lambda = Minimum eigenvalue of Hessian for Marquardt     [.01]
%        .cond   = Tolerance level for condition of Hessian        [1000]
%        .btol   = Tolerance for convergence of parm vector        [1e-4]
%        .ftol   = Tolerance for convergence of objective function [sqrt(eps)] 
%        .gtol   = Tolerance for convergence of gradient           [sqrt(eps)]
%        .prt    = Printing: 0 = None, 1 = Most, 2 = All           [0]
%       varargin = arguments list passed to func
% ------------------------------------------------------------------
% RETURNS: results = a structure variable with fields:
%           .b     = parameter value at the optimum
%           .hess  = numerical hessian at the optimum
%           .bhist = history of b at each iteration
%           .f     = objective function value at the optimum
%           .g     = gradient at the optimum
%           .dg    = change in gradient
%           .db    = change in b parameters
%           .df    = change in objective function
%           .iter  = # of iterations taken
%           .meth  = 'dfp', 'bfgs', 'gn', 'marq', 'sd' (from input)
%           .time  = time (in seconds) needed to find solution
% ------------------------------------------------------------------
% NOTES: This is really Mike Cliff's minz function converted for usage 
%        with the Econometrics Toolbox.
% calls stepz, numz, hessz functions
% ------------------------------------------------------------------

% written by:
% Mike Cliff,  UNC Finance   mcliff@unc.edu
% CREATED  12/8/98
% UPDATED  1/24/99

% changed for consistency with other
% Econometrics Toolbox optimization routines
% by:
% James P. LeSage, Dept of Economics
% University of Toledo
% 2801 W. Bancroft St,
% Toledo, OH 43606
% jlesage@spatial-econometrics.com
% last modified 4/14/2000

infoz.func = func;

% set defaults
infoz.maxit = 100;
infoz.hess = 'bfgs';
infoz.prt = 0;
infoz.cond = 1000;
infoz.btol = 1e-4;
infoz.gtol = sqrt(eps);
infoz.ftol = sqrt(eps);
infoz.lambda = 0.01;
infoz.H1 = 1;
infoz.delta = .000001;
infoz.call = 'other';
infoz.step = 'stepz';
infoz.grad='numzz'; 
hessfile = 'hesszz';


if length(info) > 0
  if ~isstruct(info)
    error('maxlik: options should be in a structure variable');
  end