The Paulsen Problem, Continuous Operator Scaling, and Smoothed Analysis

The Paulsen problem is a basic open problem in operator theory: Given vectors u1, . . . , un ∈ R d that are ǫ-nearly satisfying the Parseval’s condition and the equal norm condition, is it close to a set of vectors v1, . . . , vn ∈ R that exactly satisfy the Parseval’s condition and the equal norm condition? Given u1, . . . , un, the squared distance (to the set of exact solutions) is defined as infv ∑n i=1 ‖ui − vi‖ 2 2 where the infimum is over the set of exact solutions. Previous results show that the squared distance of any ǫ-nearly solution is at most O(poly(d, n, ǫ)) and there are ǫ-nearly solutions with squared distance at least Ω(dǫ). The fundamental open question is whether the squared distance can be independent of the number of vectors n. We answer this question affirmatively by proving that the squared distance of any ǫ-nearly solution is O(dǫ). Our approach is based on a continuous version of the operator scaling algorithm and consists of two parts. First, we define a dynamical system based on operator scaling and use it to prove that the squared distance of any ǫ-nearly solution is O(dnǫ). Then, we show that by randomly perturbing the input vectors, the dynamical system will converge faster and the squared distance of an ǫ-nearly solution is O(dǫ) when n is large enough and ǫ is small enough. To analyze the convergence of the dynamical system, we develop some new techniques in lower bounding the operator capacity, a concept introduced by Gurvits to analyzing the operator scaling algorithm. University of Waterloo. Supported by NSERC Discovery Grant 2950-120715 and NSERC Accelerator Supplement 2950-120719. Email: [email protected] University of Waterloo. Supported by NSERC Discovery Grant 2950-120715 and NSERC Accelerator Supplement 2950-120719. Email: [email protected] University of Washington and Microsoft Research. Supported by NSF award CCF-1740551. Part of this work was done while visiting University of Waterloo. Email: [email protected] University of Waterloo. Supported by NSERC Discovery Grant 2950-120715 and NSERC Accelerator Supplement 2950-120719. Email: [email protected]