Local and Linear Convergence of an Algorithm for Solving A Sparse Minimization Problem
Loading...
No Access Until
Permanent Link(s)
Collections
Other Titles
Author(s)
Abstract
For an unconstrained minimization problem with a sparse Hessian, a symmetric version of Schubert's update is given which preserves the sparseness structure defined by the Hessian. At each iteration of the algorithm there are two sparse linear systems to be solved. These have the same sparseness structure defined by the Hessian. The differences between succeeding approximations to the Hessian and the Hessian at the solution are related by a careful evaluation of the difference in the Frobenius norm. This relation is used in proving the local and linear convergence of the algorithm.
Journal / Series
Volume & Issue
Description
Sponsorship
Date Issued
1977-09
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/TR77-324
Government Document
ISBN
ISMN
ISSN
Other Identifiers
Rights
Rights URI
Types
technical report