Algorithms for optimization:
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge ; London
The MIT Press
[2019]
|
| Schlagwörter: | |
| Links: | https://algorithmsbook.com/optimization/files/optimization.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030927582&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xx, 500 Seiten Illustrationen, Diagramme (farbig) 24 cm |
| ISBN: | 9780262039420 0262039427 |
Internformat
MARC
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| 264 | 4 | |c © 2019 | |
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| 653 | 0 | |a Algorithms | |
| 653 | 6 | |a Problems and exercises | |
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Datensatz im Suchindex
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| adam_text |
Contents Preface xvii Acknowledgments xix 1 Introduction i 1.1 A History 2 1.2 Optimization Process 1.3 Basic Optimization Problem 4 1.4 Constraints 1.5 Critical Points 1.6 Conditions for Local Minima 1.7 Contour Plots 1.8 Overview 11 1.9 Summary 17 1.10 Exercises 17 5 6 7 8 11 2 Derivatives and Gradients 2.1 Derivatives 19 19
viii CONTENTS 2.2 Derivatives in Multiple Dimensions 2.3 Numerical Differentiation 23 2.4 Automatic Differentiation 27 2.5 Summary 2.6 Exercises 3 Bracketing 3.1 Unimodality 3.2 Finding an Initial Bracket 3.3 Fibonacci Search 3.4 Golden Section Search 3.5 Quadratic Fit Search 32 33 35 35 35 37 39 43 3.6 Shubert-Piyavskii Method 3.7 Bisection Method 3.8 Summary 3.9 Exercises 4 Local Descent 45 49 51 51 53 4.1 Descent Direction Iteration 4.2 Line Search 4.3 Approximate Line Search 4.4 Trust Region Methods 4.3 Termination Conditions 4.6 Summary 4.7 Exercises 53 54 66 66 55 61 63 21
CONTENTS 5 First-Order Methods 5.1 Gradient Descent 5.2 Conjugate Gradient 5.3 Momentum 5.4 Nesterov Momentum 5.5 Adagrad 5.6 RMSProp 5.7 Adadelta 5.8 Adam 5.9 Hypergradient Descent 5.10 Summary 5.11 Exercises 6 Second-Order Methods 6.1 Newton's Method 6.2 Secant Method 6.3 Quasi-Newton Methods 6.4 Summary 95 6.5 Exercises 95 7 Direct Methods 7.1 Cyclic Coordinate Search 7.2 Powell's Method 7.3 Hooke-Jeeves 7.4 Generalized Pattern Search 69 69 70 75 76 77 78 78 7g 80 84 84 87 87 91 91 99 99 100 102 103 ÎX
X CONTENTS 7-5 Nelder-Mead Simplex Method 7.6 Divided Rectangles 7-7 Summary 7-8 Exercises 8 Stochastic Methods 8.1 Noisy Descent 8.2 Mesh Adaptive Direct Search 8.3 Simulated Annealing 8.4 Cross-Entropy Method 8-5 Natural Evolution Strategies 8.6 Covariance Matrix Adaptation 8.7 Summary 8.8 Exercises 9 Population Methods 9·ΐ Initialization 9-2 Genetic Algorithms 9-3 Differential Evolution 9-4 Particle Swarm Optimization 9-5 Firefly Algorithm 9.6 Cuckoo Search 9-7 Hybrid Methods 9-8 Summary 9-9 Exercises 105 108 120 123 125 125 126 128 133 137 138 142 142 147 147 148 157 159 161 165 165 162 158
CONTENTS 167 io Constraints 10.1 Constrained Optimization 10.2 Constraint Types 10.3 Transformations to Remove Constraints 10.4 Lagrange Multipliers 10.5 Inequality Constraints 10.6 Duality 10.7 Penalty Methods 10.8 Augmented Lagrange Method 10.9 Interior Point Methods 10.10 Summary 10.11 Exercises 167 168 169 171 174 177 178 183 183 186 186 11 Linear Constrained Optimization 11.1 Problem Formulation 11.2 Simplex Algorithm 11.3 Dual Certificates 11.4 Summary 11.5 Exercises 12 Multiobjective Optimization 12.1 Pareto Optimality 12.2 Constraint Methods I2.3 Weight Methods 12.4 Multiobjective Population Methods 12.5 Preference Elicitation 12.6 Summary I2.7 Exercises 189 189 195 206 210 210 232 232 211 211 216 218 228 221
xii CONTENTS 13 Sampling Plans 13.1 Full Factorial 13.2 Random Sampling 13.3 Uniform Projection Plans 13.4 Stratified Sampling 13.5 Space-Filling Metrics 239 13.6 Space-Filling Subsets 244 13.7 Quasi-Random Sequences 13.8 Summary 13.9 Exercises 14 Surrogate Models 14.1 Fitting Surrogate Models 14.2 Linear Models 14.3 Basis Functions 14.4 Fitting Noisy Objective Functions 14.5 Model Selection 14.6 Summary 14.7 Exercises 15 Probabilistic Surrogate Models 15.1 Gaussian Distribution 13.2 Gaussian Processes 15.3 Prediction 13.4 Gradient Measurements 235 235 236 237 238 245 251 251 253 253 254 255 263 264 274 274 275 277 280 282 275
CONTENTS 15-5 Noisy Measurements 15.6 Fitting Gaussian Processes 15-7 Summary 15.S Exercises 16 Surrogate Optimization 291 16.1 Prediction-Based Exploration 291 16.2 Error-Based Exploration 16.3 Lower Confidence Bound Exploration 16.4 Probability of Improvement Exploration 16.5 Expected Improvement Exploration 16.6 Safe Optimization ι6.γ Summary 16.8 Exercises V Optimization under Uncertainty 17.1 Uncertainty 17.2 Set-Based Uncertainty V-3 Probabilistic Uncertainty 17-4 Summary 17-5 Exercises 18 Uncertainty Propagation 18.1 Sampling Methods 18.2 Taylor Approximation 18.3 Polynomial Chaos 285 287 288 288 292 293 293 294 296 305 305 307 309 312 318 318 321 322 323 321 307 xiii
XÎV CONTENTS 18.4 Bayesian Monte Carlo 18.5 Summary 18.6 Exercises 19 Discrete Optimization 19.1 Integer Programs 19.2 Rounding 19·3 Cutting Planes 19-4 Branch and Bound 19-5 Dynamic Programming 19.6 Ant Colony Optimization ^9-7 Summary 19.8 Exercises 20 Expression Optimization 20.1 Grammars 20.2 Genetic Programming 20.3 Grammatical Evolution 370 20.4 Probabilistic Grammars 375 20.5 Probabilistic Prototype Trees 20.6 Summary 20.7 Exercises 21 Multidisciplinary Optimization 21.1 Disciplinary Analyses 21.2 Interdisciplinary Compatibility 334 337 337 339 340 341 342 346 351 354 358 358 361 361 364 377 382 384 387 389 387
CONTENTS 21.3 Architectures 21.4 Multidisciplinary Design Feasible 21.5 Sequential Optimization 21.6 Individual Discipline Feasible 21.J Collaborative Optimization 21.8 Simultaneous Analysis and Design 21-9 Summary 393 393 396 398 403 406 407 21.10 Exercises 408 411 A Julia A.i Types A.2 Functions A-3 Control Flow A.4 Packages В Test Functions 425 B.i Ackley's Function 425 B.2 Booth's Function *■3 Branin Function 427 B.4 Flower Function 428 B.5 Michalewicz Function B.6 Rosenbrock's Banana Function B.7 Wheeler's Ridge 431 B.8 Circle Function 432 411 420 422 423 426 429 430 XV
XVÏ CONTENTS C Mathematical Concepts C.i Asymptotic Notation C.2 Taylor Expansion C.3 Convexity C.4 Norms C.5 Matrix Calculus C.6 Positive Definiteness C.7 Gaussian Distribution 439 439 Gaussian Quadrature D Solutions Index 495 435 436 C.8 Bibliography 433 447 483 442 442 443 433 |
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| author | Kochenderfer, Mykel J. 1980- Wheeler, Tim Allan |
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| id | DE-604.BV045543703 |
| illustrated | Illustrated |
| indexdate | 2025-02-27T15:01:17Z |
| institution | BVB |
| isbn | 9780262039420 0262039427 |
| language | English |
| lccn | 2018023091 |
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| physical | xx, 500 Seiten Illustrationen, Diagramme (farbig) 24 cm |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | The MIT Press |
| record_format | marc |
| spelling | Kochenderfer, Mykel J. 1980- Verfasser (DE-588)1077199945 aut Algorithms for optimization Mykel J. Kochenderfer, Tim A. Wheeler Cambridge ; London The MIT Press [2019] © 2019 xx, 500 Seiten Illustrationen, Diagramme (farbig) 24 cm txt rdacontent n rdamedia nc rdacarrier Algorithms Algorithms -- Problems, exercises, etc. Optimierung (DE-588)4043664-0 gnd rswk-swf Algorithmus (DE-588)4001183-5 gnd rswk-swf Algorithms / Problems, exercises, etc Problems and exercises Optimierung (DE-588)4043664-0 s Algorithmus (DE-588)4001183-5 s DE-604 Wheeler, Tim Allan Verfasser (DE-588)1194676790 aut Erscheint auch als Online-Ausgabe 9780262351409 https://algorithmsbook.com/optimization/files/optimization.pdf Verlag kostenfrei Volltext Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030927582&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Kochenderfer, Mykel J. 1980- Wheeler, Tim Allan Algorithms for optimization Algorithms Algorithms -- Problems, exercises, etc. Optimierung (DE-588)4043664-0 gnd Algorithmus (DE-588)4001183-5 gnd |
| subject_GND | (DE-588)4043664-0 (DE-588)4001183-5 |
| title | Algorithms for optimization |
| title_auth | Algorithms for optimization |
| title_exact_search | Algorithms for optimization |
| title_full | Algorithms for optimization Mykel J. Kochenderfer, Tim A. Wheeler |
| title_fullStr | Algorithms for optimization Mykel J. Kochenderfer, Tim A. Wheeler |
| title_full_unstemmed | Algorithms for optimization Mykel J. Kochenderfer, Tim A. Wheeler |
| title_short | Algorithms for optimization |
| title_sort | algorithms for optimization |
| topic | Algorithms Algorithms -- Problems, exercises, etc. Optimierung (DE-588)4043664-0 gnd Algorithmus (DE-588)4001183-5 gnd |
| topic_facet | Algorithms Algorithms -- Problems, exercises, etc. Optimierung Algorithmus |
| url | https://algorithmsbook.com/optimization/files/optimization.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=030927582&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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