  We consider the $(\ell_p,\ell_r)$-Grothendieck problem, which seeks to
maximize the bilinear form $y^T A x$ for an input matrix $A$ over vectors $x,y$
with $\|x\|_p=\|y\|_r=1$. The problem is equivalent to computing the $p \to
r^*$ operator norm of $A$. The case $p=r=\infty$ corresponds to the classical
Grothendieck problem. Our main result is an algorithm for arbitrary $p,r \ge 2$
with approximation ratio $(1+\epsilon_0)/(\sinh^{-1}(1)\cdot \gamma_{p^*}
\,\gamma_{r^*})$ for some fixed $\epsilon_0 \le 0.00863$. Comparing this with
Krivine's approximation ratio of $(\pi/2)/\sinh^{-1}(1)$ for the original
Grothendieck problem, our guarantee is off from the best known hardness factor
of $(\gamma_{p^*} \gamma_{r^*})^{-1}$ for the problem by a factor similar to
Krivine's defect.
  Our approximation follows by bounding the value of the natural vector
relaxation for the problem which is convex when $p,r \ge 2$. We give a
generalization of random hyperplane rounding and relate the performance of this
rounding to certain hypergeometric functions, which prescribe necessary
transformations to the vector solution before the rounding is applied. Unlike
Krivine's Rounding where the relevant hypergeometric function was $\arcsin$, we
have to study a family of hypergeometric functions. The bulk of our technical
work then involves methods from complex analysis to gain detailed information
about the Taylor series coefficients of the inverses of these hypergeometric
functions, which then dictate our approximation factor.
  Our result also implies improved bounds for "factorization through
$\ell_{2}^{\,n}$" of operators from $\ell_{p}^{\,n}$ to $\ell_{q}^{\,m}$ (when
$p\geq 2 \geq q$)--- such bounds are of significant interest in functional
analysis and our work provides modest supplementary evidence for an intriguing
parallel between factorizability, and constant-factor approximability.
