Non-Contradiction / AD-R-GSOC

Automatic Differentiation in R through Julia

Background

Automatic differentiation (AD) is a set of techniques to calculate derivatives automatically. It generally outperforms non-AD methods like symbolic differentiation and numerical approximation in speed or/and accuracy. It has important applications in many fields, like optimization, machine learning, Bayesian statistics and differential equations. Julia is a high-level, high-performance dynamic programming language for numerical computing. While there is a lack of automatic differentiation package in R, Julia has mature automatic differentiation packages, like ForwardDiff.jl for forward mode AD and ReverseDiff.jl for reverse mode AD. The aim of this project is to develop an R wrapper for the Julia AD packages ForwardDiff.jl and ReverseDiff.jl by the use of R packages which embeds Julia in R like JuliaCall.

Related work

• R package Deriv for symbolic differentiation.
• R package numDeriv for numeric differentiation. It provides numerical gradients, Jacobians, and Hessians, computed by finite differences, Richardson extrapolation, and other numerical approximation methods.
• R package radx for automatic differentiation, but it imposes restrictions on the functions that can be dealt with, and the functionality is not complete.
• R package nlsr, which is a solver for nonlinear least squares problems with some symbolic derivative features.
• From Perry de Valpine of Berkeley, we have learned that there is development in the Nimble project to incorporate AD via C++. Similar capability was in the AD Model Builder software, as described by Ben Bolker in https://cran.r-project.org/web/packages/R2admb/vignettes/R2admb.pdf.

Details of the coding project

The project is going to wrap the differentiation api of ForwardDiff.jl and the api of ReverseDiff.jl to do AD for native R functions and some Rcpp functions in an R package ADjlr, which means automatic differentiation in R through Julia.

The package ADjlr should provide an easy-to-use and well-documented user interface. A good example is the package numDeriv, which is already widely used in optimization to provide derivative information like gradient by numDeriv::grad, hessian by numDeriv::hessian and jacobian by numDeriv::jacobian. The usage of these three functions in numDeriv are consistent with each other, so users can use the functions easily. Another good example is the package optextras, which provides a consistent and easy-to-use interface to apply and check derivatives for optimization. The package ADjlr should provide an interface similar to other well-established packages to allow for easier usage, comparison and improvement of the tools.

Testing

Since one of the most important goal of this project is to ensure the correctness of automatic differentiation, so a comprehensive test set covering lots of possible functions for automatic differentiation will be written for the project, including native R functions, Julia functions through JuliaCall, Rcpp functions. In the tests, the automatic differentiation results for a (mostly) same function in Julia, R, and Rcpp will be checked against each other, and will also be checked against the numerical and symbolic differentiation results by packages like numDeriv and Deriv for correctness.

There should also be some benchmark tests, which checks the performance of the code before/after a certain commit or pull request, and compares the performance and with the numerical and symbolic differentiation results by packages like numDeriv and Deriv.

Every important commit or pull request should pass the tests. So CI systems will be configured, both on travis CI for mac os and linux, and appveyor for windows.

Expected impact

The project is expected to lead to an R wrapper for Julia's AD packages ForwardDiff.jl and ReverseDiff.jl. It should be able to do both forward mode and backward mode AD for

• native R functions with little or no modification,
• Julia functions in R through the JuliaCall package,
• "typical" Rcpp functions with little modification,
• mixing of the three kinds of functions.

Mentors

John Nash is retired Professor of Management in the Telfer School of Management, University of Ottawa. He has worked for approximately half a century in numerical methods for linear algebra and function minimization, in particular with R. See 'optim()' and packages such as optimx, Rvmmin, Rtnmin, Rcgmin, lbfgsb3 and nlsr. However, he also has been active in trying to simplify and unify access to the tools R makes available, that is, the navigation of the package space.

Hans Werner Borchers has experience with Julia and JuliaCall. There is also some notes on using Julia and JuliaCall on his github page.

Tests

Students, please do one or more of the following tests before contacting the mentors above.

• Use Julia packages ForwardDiff to get gradient and hessian functions for a simple Julia function f(x::Vector) = sum(sin, x) + sum(sqrt, x);, and evaluate the gradient and hessian functions on the vector [1.0, 1.0, 1.0, 1.0, 1.0].
• Use R package JuliaCall to get the gradient and hessian functions for an R function f <- function(x) {sum(sin(x)) + sum(sqrt(x))} through ForwardDiff, and evaluate the gradient and hessian functions on the vector rep(1, 5). This should be quite straightforward given JuliaCall already overloads R functions like sum, sin and sqrt.
• This project will also involve programming in Rcpp. You should be able to deal with JuliaObject from JuliaCall, which is an R6 object in Rcpp. For this test, write an Rcpp function to return the type string of a JuliaObject.

Solutions of tests

Students, please post a link to your test results here.

Name: Changcheng Li

Email: [e-mail], [e-mail]

Solution: https://github.com/Non-Contradiction/AD-R-GSOC/blob/master/Test.md