Library Header Image
LSE Research Online LSE Library Services

Optimal experimental design and quadratic optimisation

Haycroft, Rebecca, Pronzato, Luc, Wynn, Henry P. ORCID: 0000-0002-6448-1080 and Zhigljavski, Anthony (2008) Optimal experimental design and quadratic optimisation. Tatra Mountains Mathematical Publications, 29. pp. 115-123. ISSN 1210-3195

Full text not available from this repository.


A well known gradient-type algorithm for solving quadratic opti- mization problems is the method of Steepest Descent. Here the Steepest Descent algorithm is generalized to a broader family of gradient algorithms, where the step-length γk is chosen in accordance with a particular procedure. The asymp- totic rate of convergence of this family is studied. To facilitate the investigation we re-write the algorithms in a normalized form which enables us to exploit a link with the theory of optimum experimental design.

Item Type: Article
Official URL:
Additional Information: © 2008 The Authors
Divisions: Centre for Analysis of Time Series
Subjects: Q Science > QA Mathematics
Date Deposited: 26 Feb 2014 13:19
Last Modified: 15 Sep 2023 13:44

Actions (login required)

View Item View Item