Byrd, Richard H.Chin, Gillian M.Nocedal, JorgeOztoprak, Figen2024-07-182024-07-1820160025-56101436-4646https://doi.org/10.1007/s10107-015-0965-3https://hdl.handle.net/11411/7150This paper is concerned with the minimization of an objective that is the sum of a convex function f and an regularization term. Our interest is in active-set methods that incorporate second-order information about the function f to accelerate convergence. We describe a semismooth Newton framework that can be used to generate a variety of second-order methods, including block active set methods, orthant-based methods and a second-order iterative soft-thresholding method. The paper proposes a new active set method that performs multiple changes in the active manifold estimate at every iteration, and employs a mechanism for correcting these estimates, when needed. This corrective mechanism is also evaluated in an orthant-based method. Numerical tests comparing the performance of three active set methods are presented.eninfo:eu-repo/semantics/closedAccessThresholding AlgorithmNewtonShrinkageStrategyOnlineArticle2-s2.0-8494900899110.1007/s10107-015-0965-34671.ŞubQ1435159Q1WOS:000382053900014