README.md

QuasiNewton

A collection of Newton-type optimization algorithms.

Authors: Cooper Simpson

License & Citation

All source code is made available under an MIT license. You can freely use and modify the code, without warranty, so long as you provide attribution to the authors. See LICENSE for the full text.

This repository can be cited using the GitHub action in the sidebar, or using the metadata in CITATION.cff. See Publications for a full list of publications related to R-SFN and influencing this package. If any of these are useful to your own work, please cite them individually.

Installation

This package can be installed just like any other Julia package. From the terminal, after starting the Julia REPL, run the following:

using Pkg
Pkg.add("QuasiNewton")

This will install the package and its direct dependencies, but in order to use the package you must install one of the following sets of packages for automatic differentiation (AD):

• Enzyme.jl
• ReverseDiff.jl and ForwardDiff.jl
• Zygote.jl and ForwardDiff.jl

Testing

To test the package, run the following command in the REPL:

using Pkg
Pkg.test(test_args=["optional specific tests"])

Usage

Load the package as usual:

using QuasiNewton

which will export the struct RSFNOptimizer and the minimize! function. Then load your AD packages, which will export a subtype of HvpOperator. Say you load Enzyme:

using Enzyme

then the EHvpOperator will be available.

Let's look at a two dimensional Rosenbrock example:

function rosenbrock(x)

	res = 0.0
	for i = 1:size(x,1)-1
		res += 100*(x[i+1]-x[i]^2)^2 + (1-x[i])^2
	end

	return res

end

x = [0.0, 0.0]

opt = RSFNOptimizer(size(x,1))

minimize!(opt, x, rosenbrock, itmax=10)

Publications

Regularized Saddle-Free Newton: Saddle Avoidance and Efficient Implementation

@mastersthesis{rsfn,
	title = {{Regularized Saddle-Free Newton: Saddle Avoidance and Efficient Implementation}},
	author = {Cooper Simpson},
	school = {Dept. of Applied Mathematics, CU Boulder},
	year = {2022},
	type = {{M.S.} Thesis}
}