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Evaluating derivatives : Principles and techniques of algorithmic differentiation Andreas Griewank, Andrea Walther

By: Contributor(s): Publication details: Philadelphia, PA : Society for Industrial and Applied Mathematics, c2008.Edition: 2nd edDescription: xxi, 438 p. ill. 26 cmISBN:
  • 9780898716597 (alk. paper)
  • 0898716594 (alk. paper)
Subject(s): DDC classification:
  • 515.33
LOC classification:
  • QA304
Other classification:
Online resources:
Contents:
Introduction -- A framework for evaluating functions -- Fundamentals of forward and reverse -- Memory issues and complexity bounds -- Repeating and extending reverse -- Implementation and software -- Sparse forward and reverse -- Exploiting sparsity by compression -- Going beyond forward and reverse -- Jacobian and Hessian accumulation -- Observations on efficiency -- Reversal schedules and checkpointing -- Taylor and tensor coefficients -- Differentiation without differentiability -- Implicit and iterative differentiation -- Epilogue.
Summary: This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity.
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Holdings
Item type Current library Call number Copy number Status Date due Barcode
Carti IMAR 515.33-GRI (Browse shelf(Opens below)) 1 Available 0035677

eng

Bibliografie p. 411
Index p. 433

Introduction -- A framework for evaluating functions -- Fundamentals of forward and reverse -- Memory issues and complexity bounds -- Repeating and extending reverse -- Implementation and software -- Sparse forward and reverse -- Exploiting sparsity by compression -- Going beyond forward and reverse -- Jacobian and Hessian accumulation -- Observations on efficiency -- Reversal schedules and checkpointing -- Taylor and tensor coefficients -- Differentiation without differentiability -- Implicit and iterative differentiation -- Epilogue.

This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity.

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