  In this article we propose a shooting algorithm for a class of optimal
control problems for which all control variables appear linearly. The shooting
system has, in the general case, more equations than unknowns and the
Gauss-Newton method is used to compute a zero of the shooting function. This
shooting algorithm is locally quadratically convergent if the derivative of the
shooting function is one-to-one at the solution. The main result of this paper
is to show that the latter holds whenever a sufficient condition for weak
optimality is satisfied. We note that this condition is very close to a second
order necessary condition. For the case when the shooting system can be reduced
to one having the same number of unknowns and equations (square system) we
prove that the mentioned sufficient condition guarantees the stability of the
optimal solution under small perturbations and the invertibility of the
Jacobian matrix of the shooting function associated to the perturbed problem.
We present numerical tests that validate our method.
