David F. Shanno

An Interior-Point Algorithm for Nonconvex Nonlinear Programming

Interior-Point Methods for Nonconvex Nonlinear Programming: Filter Methods and Merit Functions

Recent Advances in Numerical Techniques for Large-Scale Optimization

Conditioning 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...

Matrix conditioning and nonlinear optimization

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...