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Approximating Operator Norms via Generalized Krivine Rounding

by   Vijay Bhattiprolu, et al.
NYU college
Carnegie Mellon University
Toyota Technological Institute at Chicago

We consider the (ℓ_p,ℓ_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 → r^* operator norm of A. The case p=r=∞ corresponds to the classical Grothendieck problem. Our main result is an algorithm for arbitrary p,r > 2 with approximation ratio (1+ϵ_0)/(^-1(1)·γ_p^* γ_r^*) for some fixed ϵ_0 < 0.00863. Comparing this with Krivine's approximation ratio of (π/2)/^-1(1) for the original Grothendieck problem, our guarantee is off from the best known hardness factor of (γ_p^*γ_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 > 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 ℓ_2^ n" of operators from ℓ_p^ n to ℓ_q^ m (when p≥ 2 ≥ 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.


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