The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation:
Gespeichert in:
Beteilige Person: | |
---|---|
Format: | Hochschulschrift/Dissertation Buch |
Sprache: | Englisch |
Veröffentlicht: |
München
Verlag Dr. Hut
2021
|
Ausgabe: | 1. Auflage |
Schriftenreihe: | Informatik
|
Schlagwörter: | |
Links: | http://deposit.dnb.de/cgi-bin/dokserv?id=f1380b7858b1469db81b3f7c2a7e5206&prov=M&dok_var=1&dok_ext=htm http://www.dr.hut-verlag.de/978-3-8439-4810-4.html http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032815574&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
Umfang: | vii, 162 Seiten Diagramme 24 cm x 17 cm, 476 g |
ISBN: | 9783843948104 3843948100 |
Internformat
MARC
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245 | 1 | 0 | |a The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation |c Johannes Willkomm |
250 | |a 1. Auflage | ||
264 | 1 | |a München |b Verlag Dr. Hut |c 2021 | |
300 | |a vii, 162 Seiten |b Diagramme |c 24 cm x 17 cm, 476 g | ||
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490 | 0 | |a Informatik | |
502 | |b Dissertation |c Technische Universität Darmstadt |d 2021 | ||
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Datensatz im Suchindex
DE-BY-TUM_call_number | 0001 2021 A 3779 |
---|---|
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DE-BY-TUM_location | Mag |
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adam_text | __ The reverse mode of automatic
differentiation applied to the MATLAB
language — advanced methods for
adjoint code generation
Vom Fachbereich Informatik
der Technischen Universitat Darmstadt
zur Erlangung des akademischen Grades eines
Doctor rerum naturalium (Dr rer nat )
genehmigte
DissERTATION
von
Johannes Willkomm, Dipl -Inf
aus
Aachen
Contents
Preface oo ee
Contents 000
Introduction
Ll MATLAB
111 MATLAB language 2 6 ee ee
112 Alternative interpreters and similar languages -
1 2 The reverse mode of automatic differentiation --- 5-
1 3 Scientific contributions of the author in this work 2 2 2200
1 4 Structure of this work 000 seen een
ADiMat
21 How AD works 0 0 0c Hm nennen
2 2 What AD can doand where ADisemployed --- 2- 5555
2 3 The design and development of ADiMat -- -- +--+ eee
2 4 Releated work 0 00 ee
24 1 Otherlanguages eee nenn
242 Software thatincorporates AD - ren ee rennen
243 ADiMatnamesakes 2 ee nenn
25 ADiMatusecases 0 nree een
2 6 Derivativeclasses 2 0
2 7 Forward mode source transformation 1 1 eee ee ee
2 8 Reverse mode source transformation 1 ee ee ee ee
2 9 The ADiMat transformation server 2 ee ee eee
2 10 Stacks for the reversemode 222 ee ee ee es
2 11 Alternative derivative evaluations 0005 ee eee eee
2 12 Taylor propagation © ee
2 13 Hessianevaluation 1 ee
2 13 1 Alternative Hessian evaluation modes 2
2 13 2 Hessian of Lagrangian -- 1 ee ee
Adjoint code generator techniques
3 1 Data model and structural manipulations - - -4
3 2 Binary scalarexpansion : -0--rreeereneer en nn en
321 Generalized binary scalar expansion 6 eee ee ees
322 Automatic generalized binary scalar expansion in Octave
3 3 Indexed expressions and assignments --- 2-2 eee ee eee
Vv
CONTENTS
vi
331 Multiple pairs of parentheses in expressions
3 4 Optimization 2 2 2
3 5 Complex expansion
Efficient I/O for the reverse mode
4 1 Introduction 0 ee
4 2 Related work on I/O in high-performance computing
4 3 Theneed for accessing datainreverseorder 2222 200
4 4 An interface between RIOS and automatic differentiation tools
441 Stack interfaces in Tapenade
44 2 Stackinterfacesin ADiMat 00
443 Common backend for ADiMat and Tapenade stacks
4 5 RIOS: A custom stream buffer for reverse reading
451 File/O facilitiesinCand C++ 2 2 nn
45 2 Buffering strategies offilel/OinlC 2 2 2
45 3 Buffering strategies of le /OinC4+ 2222
454 Design and implementation of custom stream buffers
455 Architecture and buffering strategy of a special-purpose stream
buffer for reverse reading
4 6 Performance results 02020 00000 eee eee
46 1 Test A: Artificial simulationcode 0
462 Test B: Solution of Burgers equation
4 7 Conclusion and Future work
Differentiation of selected MATLAB toolbox functions
5 1 Generic approaches to the differentiation of toolbox functions and builtins
511 Arithmetic propagation
512 Structural propagation 1 2 ees
513 Algorithmic propagation
5 2 Case study: Legendre functions 2 22 2 0 2-200 0-00004
5 3 Case study: the multiplication operators
531 Component-wise multiplication
532 Matrix multiplication © 2 ee
533 Convolution 0 06 ee
534 Kronecker product
Treeprocessing with XML and XSLT for AD and similar tasks
6 1 XMLterms and definitions 000 00000000045
611 XML documents with the leaf text property -
6 2 XPath and XSLT terms and definitions 000,
621 The literal output principle of XSLT ----
63 The expressive level of XML compared to other data structures
6 4 The expressive level of XSLT compared to other languages
6 5 ASTrepresentationinXML ---- 6 eee eee eee eee eee
651 XML AST elements and namespaces - 00--
652 XMLASTexamples ---- eee eee eee eee eee
653 Abstract XML AST elements and namespaces
6 6 XSLT processing steps for ASTXML ----- 2 ee ee eee eee
CONTENTS
6 7 The suspension bridge design model for the adjoint code generator 113
6 8 Facilitating XML and XSLT processing for problem solving 116
681 Setting up XSLT pipelines 2 2 ee ee, 118
68 2 P2X 2 ee 119
68 3 R2X 2 0 ee 120
6 9 Generative programming with XSLT 00 0 123
691 Generating XML pipeline definitions 125
6 10 Case study: The XC electronic document system 125
6 10 1 Production use of the XC system at fionecGmbH 127
6 11 XML document types, schemas and validation 2222222000 127
7 Complex arithmetic 131
7 1 Methods to evaluate derivatives of non-analytic complex arithmetic 135
7 2 Complex arithmetic in forward-mode AD 200020 00 4 137
7 3 Complex arithmeticinreverse-mode AD 2 2 20 138
7 4 Case Study: A fully non-analyticexample 2222 222200 140
7 5 Case study: the norm function and application to complex optimization 141
8 Conclusion 143
References 145
vii
|
any_adam_object | 1 |
author | Willkomm, Johannes |
author_GND | (DE-588)1238054102 |
author_facet | Willkomm, Johannes |
author_role | aut |
author_sort | Willkomm, Johannes |
author_variant | j w jw |
building | Verbundindex |
bvnumber | BV047414687 |
classification_rvk | ST 601 |
ctrlnum | (OCoLC)1286855995 (DE-599)DNB1237064848 |
discipline | Informatik |
edition | 1. Auflage |
format | Thesis Book |
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isbn | 9783843948104 3843948100 |
language | English |
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physical | vii, 162 Seiten Diagramme 24 cm x 17 cm, 476 g |
publishDate | 2021 |
publishDateSearch | 2021 |
publishDateSort | 2021 |
publisher | Verlag Dr. Hut |
record_format | marc |
series2 | Informatik |
spellingShingle | Willkomm, Johannes The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation MATLAB (DE-588)4329066-8 gnd Automatische Differentiation (DE-588)4314524-3 gnd Codegenerierung (DE-588)4010346-8 gnd |
subject_GND | (DE-588)4329066-8 (DE-588)4314524-3 (DE-588)4010346-8 (DE-588)4113937-9 |
title | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation |
title_auth | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation |
title_exact_search | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation |
title_full | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation Johannes Willkomm |
title_fullStr | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation Johannes Willkomm |
title_full_unstemmed | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation Johannes Willkomm |
title_short | The reverse mode of automatic differentiation applied to the MATLAB language - advanced methods for adjoint code generation |
title_sort | the reverse mode of automatic differentiation applied to the matlab language advanced methods for adjoint code generation |
topic | MATLAB (DE-588)4329066-8 gnd Automatische Differentiation (DE-588)4314524-3 gnd Codegenerierung (DE-588)4010346-8 gnd |
topic_facet | MATLAB Automatische Differentiation Codegenerierung Hochschulschrift |
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