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|Authors:||Joel Anderßon: Department of Electrical Engineering and Optimization in Engineering Center (OPTEC), K.U. Leuven, Belgium|
|Boris Houska: Department of Electrical Engineering and Optimization in Engineering Center (OPTEC), K.U. Leuven, Belgium|
|Moritz Diehl: Department of Electrical Engineering and Optimization in Engineering Center (OPTEC), K.U. Leuven, Belgium|
|Publication title:||Towards a Computer Algebra System with Automatic Differentiation for use with Object-Oriented modelling languages|
|Conference:||3rd International Workshop on Equation-Based Object-Oriented Modeling Languages and Tools; Oslo; Norway; October 3|
|Publication type:||Abstract and Fulltext|
|Abstract:||The Directed Acyclic Graph (DAG); which can be generated by object oriented modelling languages; is often the most natural way of representing and manipulating a dynamic optimization problem. With this representation; it is possible to step-by-step reformulate an (infinite dimensional) dynamic optimization problem into a (finite dimensional) non-linear program (NLP) by parametrizing the state and control trajectories.|
We introduce CasADi; a minimalistic computer algebra system written in completely self-contained C++. The aim of the tool is to offer the benifits of a computer algebra to developers of C++ code; without the overhead usually associated with such systems. In particular; we use the tool to implement automatic differentiation; AD.
For maximum efficiency; CasADi works with two different graph representations interchangeably: One supporting only scalar-valued; built-in unary and binary operations and no branching; similar to the representation used by today’s tools for automatic differentiation by operator overloading. Secondly; a representation supporting matrixvalued operations; branchings such as if-statements as well as function calls to arbitrary functions (e.g. ODE/DAE integrators).
We show that the tool performs favorably compared to CppAD and ADOL-C; two state-of-the-art tools for AD by operator overloading. We also show how the tool can be used to solve a simple optimal control problem; minimal fuel rocket flight; by implementing a simple ODE integrator with sensitivity capabilities and solving the problem with the NLP solver IPOPT. In the last example; we show how we can use the tool to couple the modelling tool Jmodelica with the optimal control software ACADO Toolkit.
|Keywords:||computer algebra system; automatic differentiation; algorithmic differentiation; dynamic optimization; Modelica|
|No. of pages:||7|
|Series:||Linköping Electronic Conference Proceedings|
|Publisher:||Linköping University Electronic Press; Linköpings universitet|
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