  We analyze the convergence rate of the multiplicative gradient (MG) method
for PET-type problems with $m$ component functions and an $n$-dimensional
optimization variable. We show that the MG method has an $O(\ln(n)/t)$
convergence rate, in both the ergodic and the non-ergodic senses. Furthermore,
we show that the distances from the iterates to the set of optimal solutions
converge (to zero) at rate $O(1/\sqrt{t})$. Our results show that, in the
regime $n=O(\exp(m))$, to find an $\varepsilon$-optimal solution of the
PET-type problems, the MG method has a lower computational complexity compared
with the relatively-smooth gradient method and the Frank-Wolfe method for
convex composite optimization involving a logarithmically-homogeneous barrier.
