Proximal quasi-Newton for computationally intensive l 1-regularized M - estimators. In Advances in Neural Information Processing Systems.Kai Zhong , Ian E. H. Yen , Inderjit S. Dhillon , Pradeep Ravikumar, Proximal quasi-Newton for computationally intensive 1-regularized M-estimators, Proceedings of...
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A PROXIMAL QUASI-NEWTON TRUST-REGION METHOD FOR NONSMOOTH REGULARIZED OPTIMIZATION 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 me... AY Aravkin,R Baraldi,D Orban - 《Siam Journ...
A bundle-type quasi-Newton method for nonconvex nonsmooth optimization In this paper, we propose a bundle-type quasi-Newton method for minimizing a nonconvex nonsmooth function. The method is based on the redistributed bundle ... C Tang,H Chent,J Jian,... - 《Pacific Journal of Optimization...
Vavasis. IMRO: A proximal quasi-newton method for solving 1-regularized least squares problems. SIAM Journal on Optimization, 27(2):583-615, Jan. 2017.Simple matlab solver for 11-regularized least squares problems. http://web.stanford.edu/-boyd/l1_ls/ . 2014...
We introduce some new proximal quasi-Newton methods for unconstrained multiobjective optimization problems (in short, UMOP), where each objective function is the sum of a twice continuously dierentiable strongly convex function and a proper lower semicontinuous convex but not necessarily dierentiable ...
Our objective of this paper is to propose a new and efficient active-set proximal quasi-Newton method for this problem. The idea behind is to speed up the convergence by separately handling the convergence of both the active and inactive variables. In particular, our method invokes a procedure...
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