Scilab Function

lsqrsolve - minimize the sum of the squares of nonlinear functions, levenberg-marquardt algorithm

### Calling Sequence

  `[x [,v [,info]]]=lsqrsolve(x0,fct,m [,stop [,diag]])`
  `[x [,v [,info]]]=lsqrsolve(x0,fct,m ,fjac [,stop [,diag]])`

### Parameters

- `x0` : real vector (initial value of functions argument).
- `fct` : external (i.e function or list or string).
- `m` : integer, the number of functions.
- `fjac` : external (i.e function or list or string).
- `stop` : optional vector `[ftol,xtol,gtol,maxfev,epsfcn,factor]` the default value is `[1.d-8,1.d-8,1.d-5,1000,0,100]`
  - `ftol` : A positive real number,termination occurs when both the actual and predicted relative reductions in the sum of squares are at most ftol. therefore, ftol measures the relative error desired in the sum of squares.
  - `xtol` : A positive real number, termination occurs when the relative error between two consecutive iterates is at most xtol. therefore, xtol measures the relative error desired in the approximate solution.
  - `gtol` : A positive real number, termination occurs when the relative error between two consecutive iterates is at most xtol. therefore, xtol measures the relative error desired in the approximate solution.
  - `maxfev` : A positive integer, termination occurs when the number of calls to fcn is at least maxfev by the end of an iteration.
  - `epsfcn` : A positive real number, used in determining a suitable step length for the forward-difference approximation. this approximation assumes that the relative errors in the functions are of the order of epsfcn. if epsfcn is less than the machine precision, it is assumed that the relative errors in the functions are of the order of the machine precision.
  - `factor` : A positive real number, used in determining the initial step bound. this bound is set to the product of factor and the euclidean norm of diag*x if nonzero, or else to factor itself. in most cases factor should lie in the interval (.1,100.). 100. is a generally recommended value.
- `diag` : is an array of length n. diag must contain positive entries that serve as multiplicative scale factors for the variables.
- `x :` real vector (final value of function argument, estimated zero).
- `v :` real vector (value of functions at x).
- `info` : termination indicator
  - `0` : improper input parameters.
  - `1` : algorithm estimates that the relative error between x and the solution is at most tol.
  - `2` : number of calls to fcn reached
  - `3` : tol is too small. No further improvement in the approximate solution x is possible.
  - `4` : iteration is not making good progress.

### Description

minimize the sum of the squares of m nonlinear functions in n variables by a modification of the levenberg-marquardt algorithm. the user must provide a subroutine which calculates the functions. the jacobian is then calculated by a forward-difference approximation.

minimize `sum(fct(x,m).^2)` where `fct` is function from `R^n` to `R^m`

`fct` should be :

  a Scilab function whose calling sequence is `v=fct(x,m)` given `x` and `m` .
  a character string which refers to a C or Fortran routine which must be linked to Scilab.

  Fortran calling sequence should be `fct(m,n,x,v,iflag)` where `m` , `n` , `iflag` are integers, `x` a double precision vector of size `n` and `v` a double precision vector of size `m` .

  C calling sequence should be `fct(int *m, int *n, double x[],double v[],int *iflag)`

`fjac` is an external which returns `v=d(fct)/dx (x)` . it should be :

  a Scilab functionwhose calling sequence is `J=fjac(x,m)` given `x` and `m` .
  a character stringit refers to a C or Fortran routine which must be linked to Scilab.

  Fortran calling sequence should be `fjac(m,n,x,jac,iflag)` where `m` , `n` , `iflag` are integers, `x` a double precision vector of size `n` and `jac` a double precision vector of size `m*n` .

  C calling sequence should be `fjac(int *m, int *n, double x[],double v[],int *iflag)`

return -1 in iflag to stop the algoritm if the function or jacobian could not be evaluated.

### Examples

    // A simple example with lsqrsolve
    a=[1,7;
       2,8
       4 3];
    b=[10;11;-1];
    function y=f1(x,m),y=a*x+b;endfunction
    [xsol,v]=lsqrsolve([100;100],f1,3)
    xsol+a\b

    function y=fj1(x,m),y=a;endfunction
    [xsol,v]=lsqrsolve([100;100],f1,3,fj1)
    xsol+a\b

    // Data fitting problem
    // 1 build the data
    a=34;b=12;c=14;
    deff('y=FF(x)','y=a*(x-b)+c*x.*x');
    X=(0:.1:3)';Y=FF(X)+100*(rand()-.5);

    //solve
    function e=f1(abc,m)
      a=abc(1);b=abc(2),c=abc(3),
      e=Y-(a*(X-b)+c*X.*X);
    endfunction
    [abc,v]=lsqrsolve([10;10;10],f1,size(X,1));
    abc
    norm(v)

### See Also

`external` , `quapro` , `linpro` , `optim` , `fsolve` ,