  We suggest simple implementable modifications of conditional gradient and
gradient projection methods for smooth convex optimization problems in Hilbert
spaces. Usually, the custom methods attain only weak convergence. We prove
strong convergence of the new versions and establish their complexity
estimates, which appear similar to the convergence rate of the weakly
convergent versions.
