Title
Evaluating Derivatives: Principles And Techniques Of Algorithmic Differentiation (Frontiers In Applied Mathematics, Series Numbe,Used
Delivery time: 8-12 business days (International)
Product Description Algorithmic, Or Automatic, Differentiation (Ad) Is Concerned With The Accurate And Efficient Evaluation Of Derivatives For Functions Defined By Computer Programs. No Truncation Errors Are Incurred, And The Resulting Numerical Derivative Values Can Be Used For All Scientific Computations That Are Based On Linear, Quadratic, Or Even Higher Order Approximations To Nonlinear Scalar Or Vector Functions. In Particular, Ad Has Been Applied To Optimization, Parameter Identification, Equation Solving, The Numerical Integration Of Differential Equations, And Combinations Thereof. Apart From Quantifying Sensitivities Numerically, Ad Techniques Can Also Provide Structural Information, E.G., Sparsity Pattern And Generic Rank Of Jacobian Matrices. Book Description Covers The Fundamentals Of Ad And Its Software, Methods For Sparse Problems, Higher Derivatives, Nonsmooth Problems, And Program Reversal Schedules. From The Publisher This Volume Will Be Valuable For Designers And Users Of Algorithms And Software For Nonlinear Computational Problems. It Opens Up An Exciting Opportunity To Develop New Algorithms That Reflect The Availability Of Accurate Derivatives And Their True Cost To Achieve Improvements In Speed And Reliability. Some Familiarity With Modern Approaches To The Seemingly Straightforward Task Of Evaluating Derivatives Will Benefit Any Mathematician, Scientist Or Engineer. About The Author Andreas Griewank Is A Former Senior Scientist Of The Mathematics And Computer Science Division, Argonne National Laboratory. He Is Currently A Professor At The Institute Of Scientific Computing In The Department Of Mathematics At The Technical University Dresden.
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This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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