CPL - Chalmers Publication Library

# Asymptotically Exact Error Analysis for the Generalized l(2)(2)-LASSO

C. Thrampoulidis ; Ashkan Panahi (Institutionen för data- och informationsteknik, Datavetenskap (Chalmers)) ; B. Hassibi
2015 IEEE International Symposium on Information Theory (ISIT), Hong Kong, CHINA, JUN 14-19, 2015 p. 2021-2025. (2015)
[Konferensbidrag, refereegranskat]

Given an unknown signal x(0) is an element of R-n and linear noisy measurements y = Ax(0) + sigma v is an element of R-m, the generalized l(2)(2)-LASSO solves (x) over cap : = arg min(x) 1/2 vertical bar vertical bar y - Ax vertical bar vertical bar(2)(2) + sigma lambda f(x). Here, f is a convex regularization function (e.g. l(1)-norm, nuclearnorm) aiming to promote the structure of x(0) (e.g. sparse, low-rank), and, lambda >= 0 is the regularizer parameter. A related optimization problem, though not as popular or well-known, is often referred to as the generalized l(2)-LASSO and takes the form (x) over cap : = arg min(x) 1/2 vertical bar vertical bar y - Ax vertical bar vertical bar(2)(2) + lambda f(x), and has been analyzed by Oymak, Thrampoulidis and Hassibi. Oymak et al. further made conjectures about the performance of the generalized l(2)(2)-LASSO. This paper establishes these conjectures rigorously. We measure performance with the normalized squared error NSE (sigma) : = vertical bar vertical bar(x) over cap - x(0) vertical bar 2 2/(m sigma(2)). Assuming the entries of A are i.i.d. Gaussian N (0,1/m) and those of v are i.i.d. N (0, 1), we precisely characterize the "asymptotic NSE" a NSE : = lim(sigma -> 0) NSE (sigma) when the problem dimensions tend to infinity in a proportional manner. The role of lambda, f and x(0) is explicitly captured in the derived expression via means of a single geometric quantity, the Gaussian distance to the subdifferential. We conjecture that a NSE = sup(sigma>0) NSE (sigma). We include detailed discussions on the interpretation of our result, make connections to relevant literature and perform computational experiments that validate our theoretical findings.

Nyckelord: lasso, recovery, risk, Computer Science, Engineering