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    Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation (Paperback) By (author) Andreas Griewank, By (author) Andrea Walther

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    DescriptionAlgorithmic, or automatic, differentiation (AD) is a growing area of theoretical research and software development concerned with the accurate and efficient evaluation of derivatives for function evaluations given as computer programs. The resulting derivative values are useful for all scientific computations that are based on linear, quadratic, or higher order approximations to nonlinear scalar or vector functions. This second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity. There is also added material on checkpointing and iterative differentiation. To improve readability the more detailed analysis of memory and complexity bounds has been relegated to separate, optional chapters. The book consists of: a stand-alone introduction to the fundamentals of AD and its software; a thorough treatment of methods for sparse problems; and final chapters on program-reversal schedules, higher derivatives, nonsmooth problems and iterative processes.

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  • Full bibliographic data for Evaluating Derivatives

    Evaluating Derivatives
    Principles and Techniques of Algorithmic Differentiation
    Authors and contributors
    By (author) Andreas Griewank, By (author) Andrea Walther
    Physical properties
    Format: Paperback
    Number of pages: 460
    Width: 174 mm
    Height: 247 mm
    Thickness: 20 mm
    Weight: 810 g
    ISBN 13: 9780898716597
    ISBN 10: 0898716594

    BIC E4L: MAT
    Nielsen BookScan Product Class 3: S7.8
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T General Subject: 710
    B&T Modifier: Academic Level: 02
    B&T Modifier: Text Format: 06, 01
    BISAC V2.8: MAT017000
    BIC subject category V2: PBWH, PBKJ
    B&T Merchandise Category: UP
    BISAC V2.8: MAT007000
    LC subject heading: , ,
    DC22: 515/.33, 515.33
    LC subject heading:
    LC classification: QA304 .G76 2008
    Thema V1.0: PBKJ, PBWH
    2, Revised
    Edition statement
    2nd Revised edition
    Illustrations note
    Society for Industrial & Applied Mathematics,U.S.
    Imprint name
    Society for Industrial & Applied Mathematics,U.S.
    Publication date
    06 November 2008
    Publication City/Country
    New York
    Author Information
    Andreas Griewank is a former senior scientist of Argonne National Laboratory and authored the first edition of this book in 2000. He holds a Ph.D. from the Australian National University and is currently Deputy Director of the Institute of Mathematics at Humboldt University Berlin and a member of the DFG Research Center Matheon, Mathematics for Key Technologies. His main research interests are nonlinear optimization and scientific computing. Andrea Walther studied mathematics and economy at the University of Bayreuth. She holds a doctorate degree from the Technische Universitat Dresden. Since 2003 Andrea Walther has been Juniorprofessor for the analysis and optimization of computer models at the Technische Universitat Dresden. Her main research interests are scientific computing and nonlinear optimization.
    Table of contents
    Rules; Preface; Prologue; Mathematical symbols; 1. Introduction; 2. A framework for evaluating functions; 3. Fundamentals of forward and reverse; 4. Memory issues and complexity bounds; 5. Repeating and extending reverse; 6. Implementation and software; 7. Sparse forward and reverse; 8. Exploiting sparsity by compression; 9. Going beyond forward and reverse; 10. Jacobian and Hessian accumulation; 11. Observations on efficiency; 12. Reversal schedules and checkpointing; 13. Taylor and tensor coefficients; 14. Differentiation without differentiability; 15. Implicit and iterative differentiation; Epilogue; List of figures; List of tables; Assumptions and definitions; Propositions, corollaries, and lemmas; Bibliography; Index.