Description

In mathematics and computer algebra, automatic differentiation (AD) is a set of techniques to numerically evaluate the derivative of a function specified by a computer program. Automatic differentiation is an alternative technique to Symbolic differentiation and Numerical differentiation (the method of finite differences). CLAD is based on Clang which will provide the necessary facilities for code transformation. The AD library is able to differentiate non-trivial functions, to find a partial derivative for trivial cases and has good unit test coverage.

Task ideas and expected results:

• The student should teach AD how to generate generate gradients computation in one function for a given function to differentiate.
• The implementation should be very well-tested and documented. The student should prepare a final poster of the work and be ready to present it.

Requirements: Advanced C++, Clang abstract syntax tree (AST), basic math.

Mentors:

• Vassil Vassilev

Corresponding Project

• ROOT

Participating Organizations

• Princeton

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