Error Bounds and Superlinear Convergence Analysis of Some Newton-Type Methods in Optimization

By Paul Tseng. 2000

62 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/493a374211f6a9df993c994597ecf2a9146ce778

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Paul Tseng
2. Jein-Shan Chen
3. Wotao Yin
4. Yangyang Xu
5. Ernö Robert Csetnek
6. Radu Ioan Bot
7. Stephen J. Wright
8. Sangwoon Yun

In Numerical Analysis

1. Paul Tseng
2. Robert Tibshirani
3. Stephen J. Wright
4. Peter Richtárik
5. Wotao Yin
6. Jieping Ye
7. Hui Zou
8. Lin Xiao

Citations in the corpus

• Approximation accuracy, gradient methods, and error bound for structured convex optimization (2010, background): “Under the EB condition, asymptotic linear and even superlinear convergence can be established for various methods, including interior point, gradient projection, proximal minimization, coordinate minimization, and coordinate gradient methods – even if X̄ is unbounded; see [12, 49, 80, 81, 139, 144, ”