Source: Rev. Mat. Iberoamericana Volume 27, Number 2
(2011), 449-474.
In this work, we concentrate our interest and efforts on general variational
(or optimization) problems which do not have solutions necessarily, but
which do have approximate solutions (or solutions within $\varepsilon > 0$).
We shall see how to recover all the (exact) minimizers of the relaxed
version of the original problem (by closed-convexification of the objective
function) in terms of the $\varepsilon $-minimizers of the original problem.
Applications to two approximation problems in a Hilbert space setting will
be shown.
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