Solving large-scale optimization problems with EFCOSS

Abstract

Derivatives play a prominent role in many areas of scientific computing. Traditionally, divided differences are employed to approximate derivatives, leading often to results of dubious quality at great computational expense. Automatic differentiation (AD), by contrast, is a powerful technique for accurately evaluating derivatives of functions described in a high-level programming language. AD requires little human effort and produces derivatives without truncation error. Although there is no conceptual difference between small and large codes, applying AD to programs with hundreds of thousands of lines of code is still a challenging task and requires a robust AD tool. We report on recent accomplishments of AD applied to the general-purpose finite element package SEPRAN transforming approximately 400,000 lines of Fortran77, and its integration into a prototype problem solving environment called EFCOSS supporting interoperability of simulation codes with optimization software using AD technology.