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The effect of regularization on drug-reaction relationships

journal contribution
posted on 2012-04-01, 00:00 authored by Musa MammadovMusa Mammadov, L Zhao, J Zhang
The least-squares method is a standard approach used in data fitting that has important applications in many areas in science and engineering including many finance problems. In the case when the problem under consideration involves large-scale sparse matrices regularization methods are used to obtain more stable solutions by relaxing the data fitting. In this article, a new regularization algorithm is introduced based on the Karush-Kuhn-Tucker conditions and the Fisher-Burmeister function. The Newton method is used for solving corresponding systems of equations. The advantages of the proposed method has been demonstrated in the establishment of drug-reaction relationships based on the Australian Adverse Drug Reaction Advisory Committee database.

History

Journal

Optimization

Volume

61

Issue

4

Pagination

405 - 422

Publisher

Taylor & Francis

Location

Abingdon, Eng.

ISSN

0233-1934

eISSN

1029-4945

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2012, Taylor & Francis

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