ECUADOR

The ECUADOR team studies Algorithmic Differentiation (AD) of computer programs, which aims at transforming, as mechanically as possible, a given program that computes a mathematical function F into another program that computes analytical derivatives of F. We put emphasis on the adjoint mode of AD, a sophisticated transformation that yields gradients for optimization at a remarkably low cost. This blends:

• AD theory: We study the AD models, to justify that they produce correct erivatives and to improve their performance. We also study software engineering techniques for analysis and transformation of programs.
• AD application to Scientific Computing: We adapt both the AD models and the strategies of Scientific Computing to take full advantage of AD. We validate our work on real-size applications.

We aim to produce AD code that can compete with hand-written sensitivity and adjoint programs used in the
industry. We implement our algorithms into the tool Tapenade, one of the most popular AD tools at present.