  We compute minimal bases of solutions for a general interpolation problem,
which encompasses Hermite-Pad\'e approximation and constrained multivariate
interpolation, and has applications in coding theory and security.
  This problem asks to find univariate polynomial relations between $m$ vectors
of size $\sigma$; these relations should have small degree with respect to an
input degree shift. For an arbitrary shift, we propose an algorithm for the
computation of an interpolation basis in shifted Popov normal form with a cost
of $\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ field operations, where $\omega$
is the exponent of matrix multiplication and the notation
$\mathcal{O}\tilde{~}(\cdot)$ indicates that logarithmic terms are omitted.
  Earlier works, in the case of Hermite-Pad\'e approximation and in the general
interpolation case, compute non-normalized bases. Since for arbitrary shifts
such bases may have size $\Theta(m^2 \sigma)$, the cost bound
$\mathcal{O}\tilde{~}(m^{\omega-1} \sigma)$ was feasible only with restrictive
assumptions on the shift that ensure small output sizes. The question of
handling arbitrary shifts with the same complexity bound was left open.
  To obtain the target cost for any shift, we strengthen the properties of the
output bases, and of those obtained during the course of the algorithm: all the
bases are computed in shifted Popov form, whose size is always $\mathcal{O}(m
\sigma)$. Then, we design a divide-and-conquer scheme. We recursively reduce
the initial interpolation problem to sub-problems with more convenient shifts
by first computing information on the degrees of the intermediate bases.
