Groupe d’études et de recherche en analyse des décisions

G-2021-12

A proximal quasi-Newton trust-region method for nonsmooth regularized optimization

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We develop a trust-region method for minimizing the sum of a smooth term \(f\) and a nonsmooth term \(h\), both of which can be nonconvex. Each iteration of our method minimizes a possibly nonconvex model of \(f + h\) in a trust region. The model coincides with \(f + h\) in value and subdifferential at the center. We establish global convergence to a first-order stationary point when \(f\) satisfies a smoothness condition that holds, in particular, when it has Lipschitz-continuous gradient, and \(h\) is proper and lower semi-continuous. The model of \(h\) is required to be proper, lower-semi-continuous and prox-bounded. Under these weak assumptions, we establish a worst-case \(O(1/\epsilon^2)\) iteration complexity bound that matches the best known complexity bound of standard trust-region methods for smooth optimization. We detail a special instance in which we use a limited-memory quasi-Newton model of \(f\) and compute a step with the proximal gradient method, resulting in a practical proximal quasi-Newton method. We establish similar convergence properties and complexity bound for a quadratic regularization variant, and provide an interpretation as a proximal gradient method with adaptive step size for nonconvex problems. We describe our Julia implementations and report numerical results on inverse problems from sparse optimization and signal processing. Our trust-region algorithm exhibits promising performance and compares favorably with linesearch proximal quasi-Newton methods based on convex models.

, 30 pages