Optimal Conditioning in the Convex Class of Rank Two Updates
Loading...
No Access Until
Permanent Link(s)
Collections
Other Titles
Authors
Abstract
Davidson's new quasi-Newton optimization algorithm selects the new inverse Hessian approximation H at each step to be the "optimally conditioned" member of a certain one-parameter class of rank two updates to the last inverse Hessian approximation H. In this paper, we show that virtually the same goals of conditioning can be achieved while restricting H to the convex class of updates. We therefore suggest that Davidson's algorithms using optimal conditioning, restrict the choice of H to members of the convex class.
Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
1976-08
Publisher
Cornell University
Keywords
computer science; technical report
Location
Effective Date
Expiration Date
Sector
Employer
Union
Union Local
NAICS
Number of Workers
Committee Chair
Committee Co-Chair
Committee Member
Degree Discipline
Degree Name
Degree Level
Related Version
Related DOI
Related To
Related Part
Based on Related Item
Has Other Format(s)
Part of Related Item
Related To
Related Publication(s)
Link(s) to Related Publication(s)
References
Link(s) to Reference(s)
Previously Published As
http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR76-288
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Rights URI
Types
technical report