We design a method SNL0: subspace Newton method for the $\\ell_0$-regularized optimization, and prove that its generated sequence converges to a stationary point globally under the strong smoothness condition. In addition, it is also quadratic convergent with the help of locally strong convexity...
Projected Newton Method for L1-Regularized Least Squares-:投影牛顿法的L1正则化最小二乘法—正则,一,L1,牛顿法,for,Least,least,牛顿吧,反馈意见 文档格式: .pdf 文档大小: 711.63K 文档页数: 6页 顶/踩数: 0/0 收藏人数: 0 评论次数: 0
机器学习优化算法:牛顿法 ( Newton Method )原创 修改于2017-08-01 09:54:36 2.8K0 举报 文章被收录于专栏:游遵文的专栏 参考文献 [1] 李航,统计学习方法 [2]Numerical Optimization: Understanding L-BFGS [3]Orthant-Wise Limited-memory Quasi-Newton Optimizer for L1-regularized Objectives 原创声明:本文...
In this paper, we propose an active-set proximal-Newton algorithm for solving 1 regularized convex/nonconvex optimization problems subject to box constraints. Our algorithm first relies on the KKT error to estimate the active and free variables, and then smoothly combines the proximal gradient ...
S Yun,KC Toh - 《Computational Optimization & Applications》 被引量: 170发表: 2011年 Parallel Coordinate Descent Newton Method for Efficient $L_1$-Regularized Loss Minimization Parallel Coordinate Descent Newton Method for Efficient $L_1$-Regularized Loss MinimizationArmijo line searchconvergence rateco...
还有需要注意的是,我们这里考虑的问题是 inifinite dimensional 的,所以我们需要的 semi-smooth Newton method 也是不同于有限维情况的 (好吧,其实也没有太大的不同了,唯一需要注意的是,在无限维情况下 Rademacher's theorem 不灵了,所以我们需要引入一个新的概念,也就是 Newton-differentiable)。那...
A PROXIMAL QUASI-NEWTON TRUST-REGION METHOD FOR NONSMOOTH REGULARIZED OPTIMIZATION nonsmooth optimizationnonconvex optimizationcomposite optimizationtrust-region methodsquasi-Newton methodsproximal gradient methodproximal quasi-Newton method... AY Aravkin,R Baraldi,D Orban - 《Siam Journal on Optimization A ...
An inexact smoothing Newton method is proposed for solving nonlinear inequality constrained optimization based on Kanzow's smoothing function.The constrained optimization is transformed into an equivalent equations by making use of the KKT conditions of the constrained optimization and some nonlinear compleme...
To accelerate the convergence of the gradient type method, we approximate the energy functional by its second-order Taylor expansion with a regularized term at each Newton iteration and adopt a cascadic multigrid technique for selecting initial data. It leads to a standard trust-region subproblem ...
Our bid to solving such model uses the framework of cubic regularization of Newton's method. As well known, the crux in cubic regularization is its utilization of the Hessian information, which may be computationally expensive for large-scale problems. To tackle this, we resort to approximating ...