On the Convergence and Rate of Convergence of the Conjugate Gradient Method.
Abstract
For the problem of minimizing an unconstrained function, the Conjugate Gradient Algorithm is shown to be convergent. If the function is uniformly strictly convex the ultimate rate of convergence is shown to be n-step superlinear. If the Hessian matrix is Lipschitz continuous the rate of convergence is shown to be nearly n-step quadratic. Comparison with other known results is given. (Author)
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 01, 1971
- Accession Number
- AD0728474
Entities
People
- Garth P. Mccormick
- Klaus Ritter
Organizations
- University of Wisconsin–Madison