solve a linear equation system with box constrains

How can I solve a overconstrained equation System in of the type A*x=b where A is a Matrix, b a vector, x is the solution vector. Each element has a box constrain of the type ci<xi<di. I know there are functions in the optimzation Toolbox, but unfortunatly I don't have it.

Answer 1

[Torsten]

min: sum_{i=1}^{n}{a_i^t*x-b_i}^2

Now replace x_j with lb_j*sin^2(y_j)+ub_j*cos^2(y_j) and use fminsearch to solve for the y_j.

Here, lb_j and ub_j are the lower and upper bounds for x_j.

Of course, using lsqlin to solve would be much more safe and efficient.

Best wishes

Torsten.

[Jan Klement]

Thank you Torsten,

it works slow but it works sufficiently good if the number of function calls is increased. I have tested your aproach against fminsearchbnd from the matlab central and is it always faster. Here the code if someone Needs it:

xt is the solution.

 n=100;
 lb=rand(1,n)*(-1000);
 ub=lb+rand(1,n)*1000;
 A=rand(n);
 b=rand(1,n);

 xr=fminsearch(@(x) afun2(x,A,b,lb,ub),start,optimset('MaxFunEvals',1e5,'MaxIter',1e10));
 [ysum y xt]=afun2(xr,A,b,lb,ub);

    function [ysum y xt]=afun2(x,A,b,lb,ub)
      xt=lb.*sin(x).^2+ub.*cos(x).^2;
      y=xt*A-b;
      ysum=(norm(y));
   end