Publications
9 shownConditioning of quasi-Newton methods for function minimization
Quasi-Newton methods accelerate the steepest-descent technique for function minimization by using computational history to generate a sequence of approximations to the inverse o...
Conjugate Gradient Methods with Inexact Searches
Conjugate gradient methods are iterative methods for finding the minimizer of a scalar function f(x) of a vector variable x which do not update an approximation to the inverse H...
Remark on “Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]”
article Remark on "Algorithm 500: Minimization of Unconstrained Multivariate Functions [E4]" Share on Authors: D. F. Shanno Department of Management Information Systems, College...
On the Convergence of a New Conjugate Gradient Algorithm
This paper studies the convergence of a conjugate gradient algorithm proposed in a recent paper by Shanno. It is shown that under loose step length criteria similar to but sligh...
Feature Article—Interior Point Methods for Linear Programming: Computational State of the Art
A survey of the significant developments in the field of interior point methods for linear programming is presented, beginning with Karmarkar's projective algorithm and concentr...
Frequent Co-Authors
Researcher Info
- h-index
- 9
- Publications
- 9
- Citations
- 5,608
- Institution
- Rutgers, The State University of New Jersey
External Links
Impact Metrics
h-index: Number of publications with at least h citations each.