  In many applications, it makes sense to solve the least square problems with
nonnegative constraints. In this article, we present a new multiplicative
iteration that monotonically decreases the value of the nonnegative quadratic
programming (NNQP) objective function. This new algorithm has a simple closed
form and is easily implemented on a parallel machine. We prove the global
convergence of the new algorithm and apply it to solving image super-resolution
and color image labelling problems. The experimental results demonstrate the
effectiveness and broad applicability of the new algorithm.
