A constrained least squares problem in a Hilbert space $H$ is considered. The standard Tikhonov regularization method is used.
In the case where the set of the constraints is the nonempty
intersection of a finite collection of closed convex subsets of
$H$ , an iterative algorithm is designed. The resulting sequence
is shown to converge strongly to the unique solution of the
regularized problem. The net of the solutions to the regularized
problems strongly converges to the minimum norm solution of the
least squares problem if its solution set is nonempty.