A new secant method for unconstrained optimization
Optimization and Control
2008-08-19 v1 Numerical Analysis
Abstract
We present a gradient-based algorithm for unconstrained minimization derived from iterated linear change of basis. The new method is equivalent to linear conjugate gradient in the case of a quadratic objective function. In the case of exact line search it is a secant method. In practice, it performs comparably to BFGS and DFP and is sometimes more robust.
Cite
@article{arxiv.0808.2316,
title = {A new secant method for unconstrained optimization},
author = {Stephen A. Vavasis},
journal= {arXiv preprint arXiv:0808.2316},
year = {2008}
}