A Levenberg–Marquardt Method for Nonsmooth Regularized Least Squares

Abstract

SIAM Journal on Scientific Computing, Volume 46, Issue 4, Page A2557-A2581, August 2024.
Abstract. We develop a Levenberg–Marquardt method for minimizing the sum of a smooth nonlinear least-squares term [math] and a nonsmooth term [math]. Both [math] and [math] may be nonconvex. Steps are computed by minimizing the sum of a regularized linear least-squares model and a model of [math] using a first-order method such as the proximal gradient method. We establish global convergence to a first-order stationary point under the assumptions that [math] and its Jacobian are Lipschitz continuous and [math] is proper and lower semicontinuous. In the worst case, our method performs [math] iterations to bring a measure of stationarity below [math]. We also derive a trust-region variant that enjoys similar asymptotic worst-case iteration complexity as a special case of the trust-region algorithm of Aravkin, Baraldi, and Orban [SIAM J. Optim., 32 (2022), pp. 900–929]. We report numerical results on three examples: a group-lasso basis-pursuit denoise example, a nonlinear support vector machine, and parameter estimation in a neuroscience application. To implement those examples, we describe in detail how to evaluate proximal operators for separable [math] and for the group lasso with trust-region constraint. In all cases, the Levenberg–Marquardt methods perform fewer outer iterations than either a proximal gradient method with adaptive step length or a quasi-Newton trust-region method, neither of which exploit the least-squares structure of the problem. Our results also highlight the need for more sophisticated subproblem solvers than simple first-order methods.