  In a real Hilbert space $\mathcal{H}$. Given any function $f$ convex
differentiable whose solution set $\argmin_{\mathcal{H}}\,f$ is nonempty, by
considering the Proximal Algorithm $x_{k+1}=\text{prox}_{\b_k f}(d x_k)$, where
$0<d<1$ and $(\b_k)$ is nondecreasing function, and by assuming some
assumptions on $(\b_k)$, we will show that the value of the objective function
in the sequence generated by our algorithm converges in order $\mathcal{O}
\left( \frac{1}{ \beta _k} \right)$ to the global minimum of the objective
function, and that the generated sequence converges strongly to the minimum
norm element of $\argmin_{\mathcal{H}}\,f$, we also obtain a convergence rate
of gradient toward zero. Afterward, we extend these results to non-smooth
convex functions with extended real values.
