rHanso-package: Hybrid Algorithm for Non-Smooth Optimization (HANSO)

In rHanso: An R Implementation of Hybrid Algorithm for Non-Smooth Optimization (HANSO)

Description

This package implements HANSO algorithm for optimization of smooth and nonsmooth functions. It uses both BFGS and gradient sampling methods. It also provides the users the option to use BFGS, gradient sampling and wolfe line search (both strong and weak).

Details

Package:	rHanso
Type:	Package
Version:	0.2-1
Date:	2013-07-26
License:	GPL (>= 3)

The main functions of this package are:

• hanso: Hanso algorithm

• bfgs: Minimiza function using BFGS

• gradsamp: Gradient sampling algorithm for non smooth (and smooth) functions

• linesch_sw: Strong wolfe line search

• linesch_ww: Weak wolfe line search

• nlcg: Non Linear Conjugate gradient minimization

• shor: An inplementation of Shor's R algorithm

Author(s)

Abhirup Mallik [e-mail] and Hans W Borchers

References

A.S. Lewis and M.L. Overton, Nonsmooth Optimization via BFGS, 2008. URL: http://www.cs.nyu.edu/overton/papers/

J.V. Burke, A.S. Lewis and M.L. Overton, A Robust Gradient Sampling Algorithm for Nonsmooth, Nonconvex Optimization SIAM J. Optimization 15 (2005), pp. 751-779

Examples

# Typical examples will be provided later.

rHanso documentation built on May 2, 2019, 5:26 p.m.