Abstract
This paper investigates efficient and powerful approach to the Gradient and the Hessian evaluation for complex functions. The idea is to apply the parallel GPU architecture and the Automatic Differentiation methods. In order to achieve better accuracy, the interval arithmetic is used. Considerations are based on sequential and parallel authors’ implementation. In this solution, both the AD methods: Forward and Reverse modes are employed. Computational experiments include analysis of performance and are studied on the generated test functions with a given complexity.
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References
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Kozikowski, G., Kubica, B.J. (2013). Interval Arithmetic and Automatic Differentiation on GPU Using OpenCL. In: Manninen, P., Öster, P. (eds) Applied Parallel and Scientific Computing. PARA 2012. Lecture Notes in Computer Science, vol 7782. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36803-5_37
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DOI: https://doi.org/10.1007/978-3-642-36803-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36802-8
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