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A proximal-Newton method for unconstrained convex optimization in Hilbert spaces
| Content Provider | Semantic Scholar |
|---|---|
| Author | Alves, Maicon Marques Svaiter, Benar Fux |
| Copyright Year | 2016 |
| Abstract | We propose and study the iteration-complexity of a proximal-Newton method for finding approximate solutions of the problem of minimizing a twice continuously differentiable convex function on a (possibly infinite dimensional) Hilbert space. We prove global convergence rates for obtaining approximate solutions in terms of function/gradient values. Our main results follow from an iteration-complexity study of an (large-step) inexact proximal point method for solving convex minimization problems. 2000 Mathematics Subject Classification: 90C25, 90C30, 47H05. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.optimization-online.org/DB_FILE/2016/10/5694.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Approximation algorithm Convergence (action) Convex function Convex optimization Gradient Hilbert space Iteration Local convergence Mathematical optimization Mathematics Subject Classification Newton's method Solutions |
| Content Type | Text |
| Resource Type | Article |