Module Handbook

  • Dynamischer Default-Fachbereich geändert auf INF

Course INF-14-57-K-6

Algorithmic Differentiation (2V+2U, 5.0 LP)

Course Type

SWS Type Course Form CP (Effort) Presence-Time / Self-Study
- K Lecture with exercise classes (V/U) 5.0 CP 94 h
2 V Lecture 28 h
2 U Exercise class (in small groups) 28 h
(2V+2U) 5.0 CP 56 h 94 h

Basedata

SWS 2V+2U
CP, Effort 5.0 CP = 150 h
Position of the semester 1 Sem. in WiSe
Level [6] Master (General)
Language [EN] English
Lecturers
Area of study [INF-VIS] Visualisation and Scientific Computing
Livecycle-State [NORM] Active

Possible Study achievement

  • Verification of study performance: proof of successful participation in the exercise classes (ungraded)
  • Details of the examination (type, duration, criteria) will be announced at the beginning of the course.

Contents

  • Difference between Algorithmic and Mathematical Differentiability
  • Basic Concepts of Algorithmic Differentiation (AD)
  • Forward Mode of AD
  • Reverse Mode of AD
  • Higher Order Derivatives
  • Implementation and Software
  • Source to Source vs. Operator Overloading Techniques
  • Reversal Schedules and Loop Checkpointing
  • Implicit and Iterative Differentiation

Literature

  • A. Griewank und A. Walther: Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition. SIAM 2008.
  • U. Naumann: The Art of Differentiating Computer Programs. SIAM, 2012.
  • current scientific literature.

Requirements for attendance (informal)

None

Requirements for attendance (formal)

None

References to Course [INF-14-57-K-6]

Module Name Context
[INF-14-57-M-6] Algorithmic Differentiation P: Obligatory 2V+2U, 5.0 LP
Course-Pool Name
[INF-VIS_V-KPOOL-6] Lectures of the teaching area Visualization and Scientific Computing