Numerical linear algebra:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer
2008
|
| Schriftenreihe: | Texts in applied mathematics
55 |
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Zsfassende Übers. von 2 franz. Originaltit.: "Introduction á Scilab-exercises pratiques corrigés d'algebre linéaire" und "Algèbre linéaire numérique" |
| Beschreibung: | XI, 271 S. graph. Darst. |
| ISBN: | 9780387341590 |
Internformat
MARC
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| 008 | 061027s2008 d||| |||| 00||| eng d | ||
| 020 | |a 9780387341590 |9 978-0-387-34159-0 | ||
| 035 | |a (OCoLC)255056469 | ||
| 035 | |a (DE-599)BVBBV021786514 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
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| 084 | |a SK 220 |0 (DE-625)143224: |2 rvk | ||
| 084 | |a MAT 657f |2 stub | ||
| 084 | |a 65Fxx |2 msc | ||
| 084 | |a 510 |2 sdnb | ||
| 100 | 1 | |a Allaire, Grégoire |e Verfasser |0 (DE-588)123414849 |4 aut | |
| 240 | 1 | 0 | |a Introduction à Scilab-exercises pratiques corrigés d'algebre linéaire & Algebre linéaire numérique |
| 245 | 1 | 0 | |a Numerical linear algebra |c Grégoire Allaire ; Sidi Mahmoud Kaber |
| 264 | 1 | |a New York, NY |b Springer |c 2008 | |
| 300 | |a XI, 271 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 1 | |a Texts in applied mathematics |v 55 | |
| 500 | |a Zsfassende Übers. von 2 franz. Originaltit.: "Introduction á Scilab-exercises pratiques corrigés d'algebre linéaire" und "Algèbre linéaire numérique" | ||
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Algebras, Linear |x Data processing | |
| 650 | 4 | |a Numerical analysis | |
| 650 | 0 | 7 | |a Lineare Algebra |0 (DE-588)4035811-2 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Numerische Mathematik |0 (DE-588)4042805-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Lineare Algebra |0 (DE-588)4035811-2 |D s |
| 689 | 0 | 1 | |a Numerische Mathematik |0 (DE-588)4042805-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Kaber, Sidi Mahmoud |e Verfasser |4 aut | |
| 830 | 0 | |a Texts in applied mathematics |v 55 |w (DE-604)BV002476038 |9 55 | |
| 856 | 4 | 2 | |m Digitalisierung UB Regensburg |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014999225&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-014999225 | ||
Datensatz im Suchindex
| _version_ | 1804135661964361728 |
|---|---|
| adam_text | Contents
1
Introduction
............................................... 1
1.1
Discretization of a Differential Equation
.................... 1
1.2
Least Squares Fitting
.................................... 4
1.3
Vibrations of a Mechanical System
........................ 8
1.4
The Vibrating String
.................................... 10
1.5
Image Compression by the
SVD
Factorization
............... 12
2
Definition and Properties of Matrices
...................... 15
2.1
Gram-Schmidt Orthonormalization Process
................. 15
2.2
Matrices
............................................... 17
2.2.1
Trace and Determinant
............................ 19
2.2.2
Special Matrices
................................... 20
2.2.3
Rows and Columns
................................ 21
2.2.4
Row and Column Permutation
...................... 22
2.2.5
Block Matrices
.................................... 22
2.3
Spectral Theory of Matrices
.............................. 23
2.4
Matrix Triangularization
................................. 26
2.5
Matrix Diagonalization
................................... 28
2.6
Min-Max
Principle
...................................... 31
2.7
Singular Values of a Matrix
............................... 33
2.8
Exercises
............................................... 38
3
Matrix Norms, Sequences, and Series
...................... 45
3.1
Matrix Norms and Subordinate Norms
..................... 45
3.2
Subordinate Norms for Rectangular Matrices
............... 52
3.3
Matrix Sequences and Series
.............................. 54
3.4
Exercises
............................................... 57
4
Introduction to Algorithmics
.............................. 61
4.1
Algorithms and pseudolanguage
........................... 61
4.2
Operation Count and Complexity
......................... 64
X
Contents
4.3
The Strassen Algorithm
.................................. 65
4.4
Equivalence of Operations
................................ 67
4.5
Exercises
............................................... 69
5
Linear Systems
............................................ 71
5.1
Square Linear Systems
................................... 71
5.2
Over- and Underdetermined Linear Systems
................ 75
5.3
Numerical Solution
...................................... 76
5.3.1 Floating-Point
System
............................. 77
5.3.2
Matrix Conditioning
............................... 79
5.3.3
Conditioning of a Finite Difference Matrix
............ 85
5.3.4
Approximation of the Condition Number
............. 88
5.3.5
Preconditioning
................................... 91
5.4
Exercises
............................................... 92
6
Direct Methods for Linear Systems
........................ 97
6.1
Gaussian Elimination Method
............................. 97
6.2
LU
Decomposition Method
...............................103
6.2.1
Practical Computation of the
LU
Factorization
........107
6.2.2
Numerical Algorithm
..............................108
6.2.3
Operation Count
..................................108
6.2.4
The Case of Band Matrices
.........................110
6.3
Cholesky Method
........................................112
6.3.1
Practical Computation of the Cholesky Factorization
..113
6.3.2
Numerical Algorithm
..............................114
6.3.3
Operation Count
..................................115
6.4
QR Factorization Method
................................116
6.4.1
Operation Count
..................................118
6.5
Exercises
...............................................119
7
Least Squares Problems
...................................125
7.1
Motivation
.............................................125
7.2
Main Results
...........................................126
7.3
Numerical Algorithms
....................................128
7.3.1
Conditioning of Least Squares Problems
..............128
7.3.2
Normal Equation Method
..........................131
7.3.3
QR Factorization Method
..........................132
7.3.4
Householder Algorithm
.............................136
7.4
Exercises
...............................................140
8
Simple Iterative Methods
..................................143
8.1
General Setting
.........................................143
8.2
Jacobi. Gauss-Seidel, and Relaxation Methods
..............147
8.2.1
Jacobi Method
....................................147
8.2.2
Gauss-Seidel Method
..............................148
Contents
XI
8.2.3
Successive
Overrelaxation
Method
(SOR)
.............149
8.3
The Special Case
of Tridiagonal Matrices...................
150
8.4
Discrete Laplacian
.......................................154
8.5
Programming
Iterative
Methods
...........................156
8.6
Block Methods
..........................................157
8.7
Exercises
...............................................159
9
Conjugate Gradient Method
...............................163
9.1
The Gradient Method
....................................163
9.2
Geometric Interpretation
.................................165
9.3
Some Ideas for Further Generalizations
.....................168
9.4
Theoretical Definition of the Conjugate Gradient Method
.....171
9.5
Conjugate Gradient Algorithm
............................174
9.5.1
Numerical Algorithm
..............................178
9.5.2
Number of Operations
.............................179
9.5.3
Convergence Speed
................................180
9.5.4
Preconditioning
...................................182
9.5.5
Chebyshev Polynomials
............................186
9.6
Exercises
...............................................189
10
Methods for Computing Eigenvalues
.......................191
10.1
Generalities
.............................................191
10.2
Conditioning
............................................192
10.3
Power Method
..........................................194
10.4
Jacobi Method
..........................................198
10.5
Givens-Householder Method
..............................203
10.6
QR Method
.............................................209
10.7
Lanczos Method
.........................................214
10.8
Exercises
...............................................219
11
Solutions and Programs
...................................223
11.1
Exercises of Chapter
2...................................223
11.2
Exercises of Chapter
3...................................234
11.3
Exercises of Chapter
4...................................237
11.4
Exercises of Chapter
5...................................241
11.5
Exercises of Chapter
6...................................250
11.6
Exercises of Chapter
7...................................257
11.7
Exercises of Chapter
8...................................258
11.8
Exercises of Chapter
9...................................260
11.9
Exercises of Chapter
10..................................262
References
.....................................................265
Index
..........................................................267
Index of Programs
.............................................272
X
Contents
4.3
The Strassen Algorithm
.................................. 65
4.4
Equivalence of Operations
................................ 67
4.5
Exercises
............................................... 69
5
Linear Systems
............................................ 71
5.1
Square Linear Systems
................................... 71
5.2
Over- and Underdetermined Linear Systems
................ 75
5.3
Numerical Solution
...................................... 76
5.3.1 Floating-Point
System
............................. 77
5.3.2
Matrix Conditioning
............................... 79
5.3.3
Conditioning of a Finite Difference Matrix
............ 85
5.3.4
Approximation of the Condition Number
............. 88
5.3.5
Preconditioning
................................... 91
5.4
Exercises
............................................... 92
6
Direct Methods for Linear Systems
........................ 97
6.1
Gaussian Elimination Method
............................. 97
6.2
LU
Decomposition Method
...............................103
6.2.1
Practical Computation of the
LU
Factorization
........107
6.2.2
Numerical Algorithm
..............................108
6.2.3
Operation Count
..................................108
6.2.4
The Case of Band Matrices
.........................110
6.3
Cholesky Method
........................................112
6.3.1
Practical Computation of the Cholesky Factorization
.. 113
6.3.2
Numerical Algorithm
..............................114
6.3.3
Operation Count
..................................115
6.4
QR Factorization Method
................................116
6.4.1
Operation Count
..................................118
6.5
Exercises
...............................................119
7
Least Squares Problems
...................................125
7.1
Motivation
.............................................125
7.2
Main Results
...........................................126
7.3
Numerical Algorithms
....................................128
7.3.1
Conditioning of Least Squares Problems
..............128
7.3.2
Normal Equation Method
..........................131
7.3.3
QR Factorization Method
..........................132
7.3.4
Householder Algorithm
.............................136
7.4
Exercises
...............................................140
8
Simple Iterative Methods
..................................143
8.1
General Setting
.........................................143
8.2
Jacobi, Gauss-Seidel, and Relaxation Methods
..............147
8.2.1
Jacobi Method
....................................147
8.2.2
Gauss-Seidel Method
..............................148
Contents
XI
8.2.3
Successive
Overrelaxation
Method
(SOR)
.............149
8.3
The Special Case
of Tridiagonal Matrices...................
150
8.4
Discrete Laplacian
.......................................154
8.5
Programming
Iterative
Methods
...........................156
8.6
Block Methods
..........................................157
8.7
Exercises
...............................................159
9
Conjugate Gradient Method
...............................163
9.1
The Gradient Method
....................................163
9.2
Geometric Interpretation
.................................165
9.3
Some Ideas for Further Generalizations
.....................168
9.4
Theoretical Definition of the Conjugate Gradient Method
.....171
9.5
Conjugate Gradient Algorithm
............................174
9.5.1
Numerical Algorithm
..............................178
9.5.2
Number of Operations
.............................179
9.5.3
Convergence Speed
................................180
9.5.4
Preconditioning
...................................182
9.5.5
Chebyshev Polynomials
............................186
9.6
Exercises
...............................................189
10
Methods for Computing Eigenvalues
.......................191
10.1
Generalities
.............................................191
10.2
Conditioning
............................................192
10.3
Power Method
..........................................194
10.4
Jacobi Method
..........................................198
10.5
Givens-Householder Method
..............................203
10.6
QR Method
.............................................209
10.7
Lanczos Method
.........................................214
10.8
Exercises
...............................................219
11
Solutions and Programs
...................................223
11.1
Exercises of Chapter
2...................................223
11.2
Exercises of Chapter
3...................................234
11.3
Exercises of Chapter
4...................................237
11.4
Exercises of Chapter
5...................................241
11.5
Exercises of Chapter
6...................................250
11.6
Exercises of Chapter
7...................................257
11.7
Exercises of Chapter
8...................................258
11.8
Exercises of Chapter
9...................................260
11.9
Exercises of Chapter
10..................................262
References
.....................................................265
Index
..........................................................267
Index of Programs
.............................................272
|
| adam_txt |
Contents
1
Introduction
. 1
1.1
Discretization of a Differential Equation
. 1
1.2
Least Squares Fitting
. 4
1.3
Vibrations of a Mechanical System
. 8
1.4
The Vibrating String
. 10
1.5
Image Compression by the
SVD
Factorization
. 12
2
Definition and Properties of Matrices
. 15
2.1
Gram-Schmidt Orthonormalization Process
. 15
2.2
Matrices
. 17
2.2.1
Trace and Determinant
. 19
2.2.2
Special Matrices
. 20
2.2.3
Rows and Columns
. 21
2.2.4
Row and Column Permutation
. 22
2.2.5
Block Matrices
. 22
2.3
Spectral Theory of Matrices
. 23
2.4
Matrix Triangularization
. 26
2.5
Matrix Diagonalization
. 28
2.6
Min-Max
Principle
. 31
2.7
Singular Values of a Matrix
. 33
2.8
Exercises
. 38
3
Matrix Norms, Sequences, and Series
. 45
3.1
Matrix Norms and Subordinate Norms
. 45
3.2
Subordinate Norms for Rectangular Matrices
. 52
3.3
Matrix Sequences and Series
. 54
3.4
Exercises
. 57
4
Introduction to Algorithmics
. 61
4.1
Algorithms and pseudolanguage
. 61
4.2
Operation Count and Complexity
. 64
X
Contents
4.3
The Strassen Algorithm
. 65
4.4
Equivalence of Operations
. 67
4.5
Exercises
. 69
5
Linear Systems
. 71
5.1
Square Linear Systems
. 71
5.2
Over- and Underdetermined Linear Systems
. 75
5.3
Numerical Solution
. 76
5.3.1 Floating-Point
System
. 77
5.3.2
Matrix Conditioning
. 79
5.3.3
Conditioning of a Finite Difference Matrix
. 85
5.3.4
Approximation of the Condition Number
. 88
5.3.5
Preconditioning
. 91
5.4
Exercises
. 92
6
Direct Methods for Linear Systems
. 97
6.1
Gaussian Elimination Method
. 97
6.2
LU
Decomposition Method
.103
6.2.1
Practical Computation of the
LU
Factorization
.107
6.2.2
Numerical Algorithm
.108
6.2.3
Operation Count
.108
6.2.4
The Case of Band Matrices
.110
6.3
Cholesky Method
.112
6.3.1
Practical Computation of the Cholesky Factorization
.113
6.3.2
Numerical Algorithm
.114
6.3.3
Operation Count
.115
6.4
QR Factorization Method
.116
6.4.1
Operation Count
.118
6.5
Exercises
.119
7
Least Squares Problems
.125
7.1
Motivation
.125
7.2
Main Results
.126
7.3
Numerical Algorithms
.128
7.3.1
Conditioning of Least Squares Problems
.128
7.3.2
Normal Equation Method
.131
7.3.3
QR Factorization Method
.132
7.3.4
Householder Algorithm
.136
7.4
Exercises
.140
8
Simple Iterative Methods
.143
8.1
General Setting
.143
8.2
Jacobi. Gauss-Seidel, and Relaxation Methods
.147
8.2.1
Jacobi Method
.147
8.2.2
Gauss-Seidel Method
.148
Contents
XI
8.2.3
Successive
Overrelaxation
Method
(SOR)
.149
8.3
The Special Case
of Tridiagonal Matrices.
150
8.4
Discrete Laplacian
.154
8.5
Programming
Iterative
Methods
.156
8.6
Block Methods
.157
8.7
Exercises
.159
9
Conjugate Gradient Method
.163
9.1
The Gradient Method
.163
9.2
Geometric Interpretation
.165
9.3
Some Ideas for Further Generalizations
.168
9.4
Theoretical Definition of the Conjugate Gradient Method
.171
9.5
Conjugate Gradient Algorithm
.174
9.5.1
Numerical Algorithm
.178
9.5.2
Number of Operations
.179
9.5.3
Convergence Speed
.180
9.5.4
Preconditioning
.182
9.5.5
Chebyshev Polynomials
.186
9.6
Exercises
.189
10
Methods for Computing Eigenvalues
.191
10.1
Generalities
.191
10.2
Conditioning
.192
10.3
Power Method
.194
10.4
Jacobi Method
.198
10.5
Givens-Householder Method
.203
10.6
QR Method
.209
10.7
Lanczos Method
.214
10.8
Exercises
.219
11
Solutions and Programs
.223
11.1
Exercises of Chapter
2.223
11.2
Exercises of Chapter
3.234
11.3
Exercises of Chapter
4.237
11.4
Exercises of Chapter
5.241
11.5
Exercises of Chapter
6.250
11.6
Exercises of Chapter
7.257
11.7
Exercises of Chapter
8.258
11.8
Exercises of Chapter
9.260
11.9
Exercises of Chapter
10.262
References
.265
Index
.267
Index of Programs
.272
X
Contents
4.3
The Strassen Algorithm
. 65
4.4
Equivalence of Operations
. 67
4.5
Exercises
. 69
5
Linear Systems
. 71
5.1
Square Linear Systems
. 71
5.2
Over- and Underdetermined Linear Systems
. 75
5.3
Numerical Solution
. 76
5.3.1 Floating-Point
System
. 77
5.3.2
Matrix Conditioning
. 79
5.3.3
Conditioning of a Finite Difference Matrix
. 85
5.3.4
Approximation of the Condition Number
. 88
5.3.5
Preconditioning
. 91
5.4
Exercises
. 92
6
Direct Methods for Linear Systems
. 97
6.1
Gaussian Elimination Method
. 97
6.2
LU
Decomposition Method
.103
6.2.1
Practical Computation of the
LU
Factorization
.107
6.2.2
Numerical Algorithm
.108
6.2.3
Operation Count
.108
6.2.4
The Case of Band Matrices
.110
6.3
Cholesky Method
.112
6.3.1
Practical Computation of the Cholesky Factorization
. 113
6.3.2
Numerical Algorithm
.114
6.3.3
Operation Count
.115
6.4
QR Factorization Method
.116
6.4.1
Operation Count
.118
6.5
Exercises
.119
7
Least Squares Problems
.125
7.1
Motivation
.125
7.2
Main Results
.126
7.3
Numerical Algorithms
.128
7.3.1
Conditioning of Least Squares Problems
.128
7.3.2
Normal Equation Method
.131
7.3.3
QR Factorization Method
.132
7.3.4
Householder Algorithm
.136
7.4
Exercises
.140
8
Simple Iterative Methods
.143
8.1
General Setting
.143
8.2
Jacobi, Gauss-Seidel, and Relaxation Methods
.147
8.2.1
Jacobi Method
.147
8.2.2
Gauss-Seidel Method
.148
Contents
XI
8.2.3
Successive
Overrelaxation
Method
(SOR)
.149
8.3
The Special Case
of Tridiagonal Matrices.
150
8.4
Discrete Laplacian
.154
8.5
Programming
Iterative
Methods
.156
8.6
Block Methods
.157
8.7
Exercises
.159
9
Conjugate Gradient Method
.163
9.1
The Gradient Method
.163
9.2
Geometric Interpretation
.165
9.3
Some Ideas for Further Generalizations
.168
9.4
Theoretical Definition of the Conjugate Gradient Method
.171
9.5
Conjugate Gradient Algorithm
.174
9.5.1
Numerical Algorithm
.178
9.5.2
Number of Operations
.179
9.5.3
Convergence Speed
.180
9.5.4
Preconditioning
.182
9.5.5
Chebyshev Polynomials
.186
9.6
Exercises
.189
10
Methods for Computing Eigenvalues
.191
10.1
Generalities
.191
10.2
Conditioning
.192
10.3
Power Method
.194
10.4
Jacobi Method
.198
10.5
Givens-Householder Method
.203
10.6
QR Method
.209
10.7
Lanczos Method
.214
10.8
Exercises
.219
11
Solutions and Programs
.223
11.1
Exercises of Chapter
2.223
11.2
Exercises of Chapter
3.234
11.3
Exercises of Chapter
4.237
11.4
Exercises of Chapter
5.241
11.5
Exercises of Chapter
6.250
11.6
Exercises of Chapter
7.257
11.7
Exercises of Chapter
8.258
11.8
Exercises of Chapter
9.260
11.9
Exercises of Chapter
10.262
References
.265
Index
.267
Index of Programs
.272 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Allaire, Grégoire Kaber, Sidi Mahmoud |
| author_GND | (DE-588)123414849 |
| author_facet | Allaire, Grégoire Kaber, Sidi Mahmoud |
| author_role | aut aut |
| author_sort | Allaire, Grégoire |
| author_variant | g a ga s m k sm smk |
| building | Verbundindex |
| bvnumber | BV021786514 |
| callnumber-first | Q - Science |
| callnumber-label | QA185 |
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| callnumber-search | QA185.D37 |
| callnumber-sort | QA 3185 D37 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | SK 220 |
| classification_tum | MAT 657f |
| ctrlnum | (OCoLC)255056469 (DE-599)BVBBV021786514 |
| dewey-full | 510 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 510 - Mathematics |
| dewey-raw | 510 |
| dewey-search | 510 |
| dewey-sort | 3510 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| discipline_str_mv | Mathematik |
| format | Book |
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| id | DE-604.BV021786514 |
| illustrated | Illustrated |
| index_date | 2024-07-02T15:43:05Z |
| indexdate | 2024-07-09T20:44:04Z |
| institution | BVB |
| isbn | 9780387341590 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-014999225 |
| oclc_num | 255056469 |
| open_access_boolean | |
| owner | DE-355 DE-BY-UBR DE-824 DE-573 DE-29T DE-20 DE-11 DE-83 DE-91G DE-BY-TUM DE-188 |
| owner_facet | DE-355 DE-BY-UBR DE-824 DE-573 DE-29T DE-20 DE-11 DE-83 DE-91G DE-BY-TUM DE-188 |
| physical | XI, 271 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Springer |
| record_format | marc |
| series | Texts in applied mathematics |
| series2 | Texts in applied mathematics |
| spelling | Allaire, Grégoire Verfasser (DE-588)123414849 aut Introduction à Scilab-exercises pratiques corrigés d'algebre linéaire & Algebre linéaire numérique Numerical linear algebra Grégoire Allaire ; Sidi Mahmoud Kaber New York, NY Springer 2008 XI, 271 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Texts in applied mathematics 55 Zsfassende Übers. von 2 franz. Originaltit.: "Introduction á Scilab-exercises pratiques corrigés d'algebre linéaire" und "Algèbre linéaire numérique" Datenverarbeitung Algebras, Linear Data processing Numerical analysis Lineare Algebra (DE-588)4035811-2 gnd rswk-swf Numerische Mathematik (DE-588)4042805-9 gnd rswk-swf Lineare Algebra (DE-588)4035811-2 s Numerische Mathematik (DE-588)4042805-9 s DE-604 Kaber, Sidi Mahmoud Verfasser aut Texts in applied mathematics 55 (DE-604)BV002476038 55 Digitalisierung UB Regensburg application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014999225&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Allaire, Grégoire Kaber, Sidi Mahmoud Numerical linear algebra Texts in applied mathematics Datenverarbeitung Algebras, Linear Data processing Numerical analysis Lineare Algebra (DE-588)4035811-2 gnd Numerische Mathematik (DE-588)4042805-9 gnd |
| subject_GND | (DE-588)4035811-2 (DE-588)4042805-9 |
| title | Numerical linear algebra |
| title_alt | Introduction à Scilab-exercises pratiques corrigés d'algebre linéaire & Algebre linéaire numérique |
| title_auth | Numerical linear algebra |
| title_exact_search | Numerical linear algebra |
| title_exact_search_txtP | Numerical linear algebra |
| title_full | Numerical linear algebra Grégoire Allaire ; Sidi Mahmoud Kaber |
| title_fullStr | Numerical linear algebra Grégoire Allaire ; Sidi Mahmoud Kaber |
| title_full_unstemmed | Numerical linear algebra Grégoire Allaire ; Sidi Mahmoud Kaber |
| title_short | Numerical linear algebra |
| title_sort | numerical linear algebra |
| topic | Datenverarbeitung Algebras, Linear Data processing Numerical analysis Lineare Algebra (DE-588)4035811-2 gnd Numerische Mathematik (DE-588)4042805-9 gnd |
| topic_facet | Datenverarbeitung Algebras, Linear Data processing Numerical analysis Lineare Algebra Numerische Mathematik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014999225&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| volume_link | (DE-604)BV002476038 |
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