function [x,Resnorm,FVAL,EXITFLAG,OUTPUT,LAMBDA,JACOB] = lsqnonlin(FUN,x,LB,UB,options,varargin)
%LSQNONLIN solves non-linear least squares problems.
%   LSQNONLIN attempts to solve problems of the form:
%   min  sum {FUN(X).^2}    where X and the values returned by FUN can be   
%             x             vectors or matrices.
%
%   X=LSQNONLIN(FUN,X0) starts at the matrix X0 and finds a minimum X to 
%   the sum of squares of the functions in FUN. FUN accepts input X 
%   and returns a vector (or matrix) of function values F evaluated
%   at X. NOTE: FUN should return FUN(X) and not the sum-of-squares 
%   sum(FUN(X).^2)). (FUN(X) is summed and squared implicitly in the
%   algorithm.) 
%
%   X=LSQNONLIN(FUN,X0,LB,UB) defines a set of lower and upper bounds on
%   the design variables, X, so that the solution is in the range LB <= X
%   <= UB.  Use empty matrices for LB and UB if no bounds exist. Set LB(i)
%   = -Inf if X(i) is unbounded below;  set UB(i) = Inf if X(i) is
%   unbounded above.
%
%   X=LSQNONLIN(FUN,X0,LB,UB,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,
%   FunValCheck, Jacobian, JacobMult, JacobPattern, LineSearchType,
%   LevenbergMarquardt, MaxFunEvals, MaxIter, DiffMinChange and
%   DiffMaxChange, LargeScale, MaxPCGIter, PrecondBandWidth, TolPCG,
%   TypicalX, and OutputFcn. Use the Jacobian option to specify that FUN
%   also returns a second output argument J that is the Jacobian matrix at
%   the point X. If FUN returns a vector F of m components when X has
%   length n, then J is an m-by-n matrix where J(i,j) is the partial
%   derivative of F(i) with respect to x(j). (Note that the Jacobian J is
%   the transpose of the gradient of F.)
%
%   [X,RESNORM]=LSQNONLIN(FUN,X0,...) returns 
%   the value of the squared 2-norm of the residual at X: sum(FUN(X).^2). 
%
%   [X,RESNORM,RESIDUAL]=LSQNONLIN(FUN,X0,...) returns the value of the 
%   residual at the solution X: RESIDUAL = FUN(X).
%
%   [X,RESNORM,RESIDUAL,EXITFLAG]=LSQNONLIN(FUN,X0,...) returns an EXITFLAG 
%   that describes the exit condition of LSQNONLIN. Possible values of EXITFLAG
%   and the corresponding exit conditions are 
%
%     1  LSQNONLIN converged to a solution X.
%     2  Change in X smaller than the specified tolerance.
%     3  Change in the residual smaller than the specified tolerance.
%     4  Magnitude search direction smaller than the specified tolerance.
%     0  Maximum number of function evaluations or of iterations reached.
%    -1  Algorithm terminated by the output function.
%    -2  Bounds are inconsistent.
%    -4  Line search cannot sufficiently decrease the residual along the 
%         current search direction.
%
%   [X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT]=LSQNONLIN(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,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA]=LSQNONLIN(FUN,X0,...) returns 
%   the set of Lagrangian multipliers, LAMBDA, at the solution: LAMBDA.lower 
%   for LB and LAMBDA.upper for UB.
%
%   [X,RESNORM,RESIDUAL,EXITFLAG,OUTPUT,LAMBDA,JACOBIAN]=LSQNONLIN(FUN,X0,...) 
%   returns the Jacobian of FUN at X.   
%
%   Examples
%     FUN can be specified using @:
%        x = lsqnonlin(@myfun,[2 3 4])
%
%   where MYFUN is a MATLAB function such as:
%
%       function F = myfun(x)
%       F = sin(x);
%
%   FUN can also be an anonymous function:
%
%       x = lsqnonlin(@(x) sin(3*x),[1 4])
%
%   If FUN is parameterized, you can use anonymous functions to capture the 
%   problem-dependent parameters. Suppose you want to solve the non-linear least
%   squares problem given in the function MYFUN, which is parameterized by its 
%   second argument A. Here MYFUN is an M-file function such as
%
%       function F = myfun(x,a)
%       F = [ 2*x(1) - exp(a*x(1))
%             -x(1) - exp(a*x(2))
%             x(1) - x(2) ];
%
%   To solve the least squares problem for a specific value of A, first assign 
%   the value to A. Then create a one-argument anonymous function that captures
%   that value of A and calls MYFUN with two arguments. Finally, pass this 
%   anonymous function to LSQNONLIN:
%
%       a = -1; % define parameter first
%       x = lsqnonlin(@(x) myfun(x,a),[1;1])
%
%   See also OPTIMSET, LSQCURVEFIT, FSOLVE, @, INLINE.

%   Copyright 1990-2004 The MathWorks, Inc. 
%   $Revision: 1.33.4.6 $  $Date: 2004/12/06 16:36:15 $

%   The default algorithm when OPTIONS.LargeScale = 'off' is the 
%   Levenberg-Marquardt method with a mixed quadratic and cubic line search procedure.  
%   A Gauss-Newton method is selected by setting OPTIONS.LargeScale='off' and 
%   OPTIONS.LevenbergMarquardt='off'. 
%
%

% ------------Initialization----------------


defaultopt = struct('Display','final','LargeScale','on', ...
   'TolX',1e-6,'TolFun',1e-6,'DerivativeCheck','off',...
   'Diagnostics','off','FunValCheck','off',...
   'Jacobian','off','JacobMult',[],... % JacobMult set to [] by default
   'JacobPattern','sparse(ones(Jrows,Jcols))',...
   'MaxFunEvals','100*numberOfVariables',...
   'DiffMaxChange',1e-1,'DiffMinChange',1e-8,...
   'PrecondBandWidth',0,'TypicalX','ones(numberOfVariables,1)',...
   'MaxPCGIter','max(1,floor(numberOfVariables/2))', ...
   'TolPCG',0.1,'MaxIter',400,...
   'LineSearchType','quadcubic','LevenbergMarquardt','on', ...
   'OutputFcn', []); 

% If just 'defaults' passed in, return the default options in X
if nargin==1 && nargout <= 1 && isequal(FUN,'defaults')
   x = defaultopt;
   return
end
if nargin < 2
  error('optim:lsqnonlin:NotEnoughInputs', ...
        'LSQNONLIN requires two input arguments.')
end
if nargin < 3, LB=[]; end
if nargin < 4, UB=[]; end
if nargin < 5, options=[]; end

% Check for non-double inputs
% SUPERIORFLOAT errors when superior input is neither single nor double;
% We use try-catch to override SUPERIORFLOAT's error message when input
% data type is integer.
try
    dataType = superiorfloat(x,LB,UB);
    if ~isequal('double', dataType)
        error('optim:lsqnonlin:NonDoubleInput', ...
            'LSQNONLIN only accepts inputs of data type double.')
    end
catch
    error('optim:lsqnonlin:NonDoubleInput', ...
        'LSQNONLIN only accepts inputs of data type double.')
end

if nargout > 5
   computeLambda = 1;
else 
   computeLambda = 0;
end

caller = 'lsqnonlin'; YDATA = [];
[x,Resnorm,FVAL,EXITFLAG,OUTPUT,LAMBDA,JACOB] = ...
   lsqncommon(FUN,x,YDATA,LB,UB,options,defaultopt,caller,...
              computeLambda,varargin{:});