Clad version 0.5

Introduction

This document contains the release notes for the automatic differentiation plugin for clang Clad, release 0.5. Clad is built on top of Clang and LLVM compiler infrastructure. Here we describe the status of Clad in some detail, including major improvements from the previous release and new feature work.

Note that if you are reading this file from a git checkout, this document applies to the next release, not the current one.

What's New in Clad 0.5?

Some of the major new features and improvements to Clad are listed here. Generic improvements to Clad as a whole or to its underlying infrastructure are described first.

External Dependencies

• clang-5.0

Forward & Reverse Mode

• Extend the way to specify a dependent variables. Consider function, double f(double x, double y, double z) {...}, clad::differentiate(f, "z") is equivalent to clad::differentiate(f, 2). clad::gradient(f, "x, y") differentiates with respect to x and y but not z. The gradient results are stored in a _result parameter in the same order as x and y were specified. Namely, the result of x is stored in _result[0] and the result of y in _result[1]. If we invert the arguments specified in the string to clad::gradient(f, "y, x") the results will be stored inversely.
• Enable recursive differentiation.
• Support single- and multi-dimensional arrays -- works for arrays with constant size like double A[] = {1, 2, 3};, double A[3]; or double A[1][2][3][4];

Reverse Mode

• Support variable reassignments. For example,

double f(double x, double y) {
  double a = x * x;
  double b = y * y;
  double c = a + b;
  return c;
}

Misc

• Add coverity static analyzer to the pull request builds.
• Fix found by coverity issues.
• Improved README.

Fixed Bugs

Issue 77
Issue 105

Special Kudos

This release wouldn't have happened without the efforts of our contributors,
listed in the form of Firstname Lastname (#contributions):

FirstName LastName (#commits):

• Aleksandr Efremov(7)
• Vassil Vassilev (6)
• Oksana Shadura (2)