Advances in Automatic Differentiation - download pdf or read online

By Adrian Sandu (auth.), Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke (eds.)

ISBN-10: 3540689354

ISBN-13: 9783540689355

ISBN-10: 3540689427

ISBN-13: 9783540689423

This assortment covers advances in computerized differentiation conception and perform. computing device scientists and mathematicians will find out about fresh advancements in computerized differentiation thought in addition to mechanisms for the development of strong and robust automated differentiation instruments. Computational scientists and engineers will enjoy the dialogue of varied functions, which offer perception into powerful options for utilizing automated differentiation for inverse difficulties and layout optimization.

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Extra resources for Advances in Automatic Differentiation

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This check is performed with randomly chosen should be satisfied for all A, values for these matrices. The MATLAB code for these validation checks is contained in an appendix of the extended version of this paper [6] and is available on request. Matrix Derivative Results 43 5 Conclusions This paper has reviewed a number of matrix derivative results in numerical linear algebra. These are useful in applying both forward and reverse mode algorithmic differentiation at a higher level than the usual binary instruction level considered by most AD tools.

A systematic way of discovering non-trivial loop invariants is outlined in [10, p. 280]. The rule Implied (imp) states that if we have proved {φ0 }s{ψ0 } and that φ implies φ0 and ψ is implied by ψ0 , then we can prove {φ }s{ψ }. This allows us for example to strengthen a pre-condition by adding more assumptions and to weaken a post-condition by concluding less than we can. These rules can be composed, hence allowing us to interactively prove the activity status of program variables. 30 Emmanuel M.

We sometimes gave names to certain long commands by preceding them with an identifier followed by ’:’. The notation S ⇒ T means S is transformed into T and the premise V (b) ∩ A = 0/ wherein b is a guard, ensures the source code is piecewise differentiable. To give an idea of the proof rules, consider the assignment rule. It states that if in a pre-state, a statement S, Certifying AD Transformations 31 Fig. 4. Hoare logic for the forward mode AD S : z := e(x) ⇒ T : dz := ∑ni=1 ∂ e (x) ∂ xi S1 ⇒ T1 : P(S1 ) ⇒ Q(S1 , T1 ) asgn · dxi ; z := e(x) : Q(S, T )[z/e(x)] ⇒ Q(S, T ) S2 ⇒ T2 : Q(S1 , T1 ) ∧ P(S2 ) ⇒ Q(S2 , T2 ) S : S1 ; S2 ⇒ T : T1 ; T2 : P(S) ⇒ Q(S, T ) V (b) ∩ A = 0/ S1 ⇒ T1 : P(S1 ) ∧ b ⇒ Q(S1 , T1 ) seq S2 ⇒ T2 : P(S2 ) ∧ ¬b ⇒ Q(S2 , T2 ) S : if b S1 else S2 ⇒ T : if b then T1 else T2 : P(S) ∧ (V (b) ∩ A = 0) / ⇒ Q(S, T ) s ⇒ t : P(s,t) ∧ b ∧ (V (b) ∩ A = 0) / ⇒ P(s,t) S : while b do s ⇒ T : while b do t : P(S, T ) ∧ (V (b) ∩ A = 0) / ⇒ P(S, T ) ∧ ¬b P ⇒ P0 S ⇒ T : P0 (S) ⇒ Q0 (T ) S ⇒ T : P(S) ⇒ Q(S, T ) Q0 ⇒ Q if while imp z := e(x), wherein e(x) is an expression depending on x, is transformed into the sequence T of the two assignments dz := ∑ni=1 ∂∂xei · dxi ; z := e(x), then we get the value of the lhs z and its derivative dz = ∂∂xei · x˙ in a post-state.

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Advances in Automatic Differentiation by Adrian Sandu (auth.), Christian H. Bischof, H. Martin Bücker, Paul Hovland, Uwe Naumann, Jean Utke (eds.)


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