AD Tutorial at the TU Berlin

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Test Example for the Symmetric Eigenvalue Decomposition (cont.) abs. error |λ 1 d − ˜λ 1 d | 10 −14 10 −15 10 −16 d = 0 d = 1 d = 2 d = 3 d = 4 errors of λ 1 10 −17 10 −15 10 −14 10 −13 10 −12 10 −11 10 −10 10 −9 10 −8 10 −7 10 −6 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 δ Sebastian F. Walter, HU Berlin () Not So Short Tutorial OnAlgorithmic Differentiation Wednesday, 04.06.2010 32 / 39
Test Example for the Symmetric Eigenvalue Decomposition (cont.) abs. error |λ 2 d − ˜λ 2 d | 10 −8 10 −9 10 −10 10 −11 10 −12 10 −13 10 −14 10 −15 errors of λ 2 d = 0 d = 1 d = 2 d = 3 d = 4 10 −16 10 −17 10 −18 10 −15 10 −14 10 −13 10 −12 10 −11 10 −10 10 −9 10 −8 10 −7 10 −6 10 −5 10 −4 10 −3 10 −2 10 −1 10 0 δ Sebastian F. Walter, HU Berlin () Not So Short Tutorial OnAlgorithmic Differentiation Wednesday, 04.06.2010 33 / 39
Page 1 and 2: Not So Short Tutorial On Algorithmi
Page 3 and 4: Computational Model computer progra
Page 5 and 6: PART I: The Forward Mode of AD: Tay
Page 7 and 8: Univariate Taylor Polynomial Arithm
Page 9 and 10: Algorithms for Univariate Taylor Po
Page 11 and 12: Higher-Order Mixed Partial Derivati
Page 13 and 14: PART II: The Reverse Mode of AD Seb
Page 15 and 16: Live Example: Semi-Automatic Revers
Page 17 and 18: PART III: Advanced AD and Applicati
Page 19 and 20: Optimum Experimental Design in Chem
Page 21 and 22: Differentiation of ODE Solvers Expl
Page 23 and 24: Application Example: DAESOL-II for
Page 25 and 26: Univariate Taylor Propagation on Ma
Page 27 and 28: Algorithm: Reverse UTPM of the Rect
Page 29 and 30: ALGOPY Live Example: Moore-Penrose
Page 31: Test Example for the Symmetric Eige
Page 35 and 36: Test Example for the Symmetric Eige
Page 37 and 38: Sebastian F. Walter, HU Berlin () N
Page 39: Summary: Have shown many aspects of

AD Tutorial at the TU Berlin

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