leastsq

leastsq — Solves non-linear least squaresproblems

Calling sequence

[f,xopt]=leastsq([imp,] fun [,Dfun],x0)  
[f,[xopt,[gradopt]]]=leastsq(fun [,Dfun],[contr],x0,['algo'],[df0,[mem]],  
     ,[stop],['in'])

Parameters

imp : scalar argument used to set the trace mode. imp=0 nothing (execpt errors) is reported, imp=1 initial and final reports, imp=2 adds a report per iteration, imp>2 add reports on linear search. Warning, most of these reports are written on the Scilab standard output.

fun : external, i.e Scilab function or string (fun is the function defining the least square probleme: see below.

x0 : real vector (initial value of variable to be minimized).

f : value of optimal least square value.

xopt : best value of x found.

contr : 'b',binf,bsup with binf and bsup real vectors with same dimension as x0. binf and bsup are lower and upper bounds on x.

algo : A string with possible values :'qn' or 'gc' or 'nd' . This string stands for quasi-Newton (default), conjugate gradient or non-differentiable respectively. Note that 'nd' does not accept bounds on x ).

df0 : real scalar. Guessed decreasing of f at first iteration. (df0=1 is the default value).

mem : integer, number of variables used to approximate the Hessian, (algo='gc' or 'nd'). Default value is around 6.

stop

: sequence of optional parameters controlling the convergence of the algorithm. stop= 'ar',nap, [iter [,epsg [,epsf [,epsx]]]]

`"ar"`	: reserved keyword for stopping rule selection defined as follows:
`nap`	: maximum number of calls to `fun` allowed.
`iter`	: maximum number of iterations allowed.
`epsg`	: threshold on gradient norm.
`epsf`	: threshold controlling decreasing of `f`
`epsx`	: threshold controlling variation of `x`. This vector (possibly matrix) of same size as `x0` can be used to scale `x`.

"in" : reserved keyword for initialization of parameters used when fun in given as a Fortran routine (see below).

gradopt : gradient of fun at xopt

Description

Non-linear optimization routine for programs without constraints or with bound constraints:

min sum(f(x)).^2 w.r.t x, Here, f is a function from R^n to R^m which returns f(x), a real vector ( value of function at x).

fun

gives the definition of the function f(x). It is an "external" i.e function, or list or C or Fortran routine (see "external").

This external must return a vector y such as (y(j)=f(x)) for a given x.

If fun is a Scilab function, the calling sequence for fun must be: y=fun(x, [optional parameters])

If

fun is defined by a Fortran or C routine first argument must be a list: list(fun_name,m,p1,..pl)

fun_name is then a character string, it refers to the name of the routine which must be linked to Scilab. Here, the generic calling sequences are:

In Fortran



          subroutine fun(n,m,x,td,y)
          integer n,m
          double precision x(n), td(*), y(m)

In C



          void fun(int *n,int *m, double *x,double *params, double *y)

n is the dimension of vector x , m is the dimension of vectory, td is a vector which contains the parameters p1,..pl

Dfun

Gives the definition of the function df(x)/dx. It is an "external".This external must return a matrix g such as (g(i,j)=dfi/dxj) for a given x.

If Dfun is a function, the calling sequence for fun must be: g=Dfun(x, [optional parameters]) .

If

Dfun is defined by a Fortran or C routine first argument must be a list: list(fun_name,m,...)

fun_name is a character string, it refers to the name of the routine which must be linked to Scilab. This function has the same calling sequence as fun

Examples



a=rand(3,2);b=[1;1;1];x0=[1;-1];
deff('f=fun(x,a,b)','f=a*x-b');
deff('g=dfun(x,a,b)','g=a');

[f,xopt]=leastsq(fun,x0)      //Simplest call
xopt-a\b  //compare with linear algebra solution

[f,xopt]=leastsq(fun,dfun,x0)      //specify gradient

[f,xopt]=leastsq(list(fun,[1 2;3 4],[1;2]),x0)    

deff('f=fun(x,a,b)','f=exp(a*x)-b');
deff('g=dfun(x,a,b)','g=a.*(exp(a*x)*ones(1,size(a,2)))');

[f,xopt]=leastsq(list(fun,[1 2;3 4],[1;2]),x0)

leastsq

Calling sequence

Parameters

Description

Examples

See also