  Active set method aims to find the correct active set of the optimal solution
and it is a powerful method for solving strictly convex quadratic problem with
bound constraints. To guarantee the finite step convergence, the existing
active set methods all need strict conditions or some additional strategies,
which greatly affect the efficiency of the algorithm. In this paper, we propose
a random active set method which introduces randomness in the update of active
set. We prove that it can converge in finite iterations with probability one
without any conditions on the problem or any additional strategies. Numerical
results show that the algorithm obtains the correct active set within a few
iterations, and compared with the existing methods, it has better robustness
and efficiency.
