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Optimization for learning and control /
"Comprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Hoboken, New Jersey :
John Wiley & Sons, Inc.,
[2023]
|
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a2200000 i 4500 | ||
|---|---|---|---|
| 001 | OR_on1370502088 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
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| 008 | 230124s2023 njua ob 001 0 eng | ||
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| 020 | |a 9781119809173 |q electronic book | ||
| 020 | |a 1119809177 |q electronic book | ||
| 020 | |a 9781119809180 |q electronic book | ||
| 020 | |a 1119809185 |q electronic book | ||
| 020 | |a 9781119809142 |q electronic book | ||
| 020 | |a 1119809142 |q electronic book | ||
| 020 | |z 9781119809135 |q hardcover | ||
| 024 | 7 | |a 10.1002/9781119809180 |2 doi | |
| 029 | 1 | |a AU@ |b 000074405737 | |
| 035 | |a (OCoLC)1370502088 | ||
| 037 | |a 9781119809135 |b O'Reilly Media | ||
| 037 | |a 10132902 |b IEEE | ||
| 042 | |a pcc | ||
| 050 | 0 | 4 | |a T57.62 |b .H43 2023 |
| 082 | 0 | 0 | |a 004.2/10151 |2 23/eng/20230202 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Hansson, Anders, |e author. | |
| 245 | 1 | 0 | |a Optimization for learning and control / |c Anders Hansson, Martin Andersen. |
| 264 | 1 | |a Hoboken, New Jersey : |b John Wiley & Sons, Inc., |c [2023] | |
| 300 | |a 1 online resource (xxvii, 397 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 520 | |a "Comprehensive resource providing a masters' level introduction to optimization theory and algorithms for learning and control Optimization for Learning and Control describes how optimization is used in these domains, giving a thorough introduction to both unsupervised learning, supervised learning, and reinforcement learning, with an emphasis on optimization methods for large-scale learning and control problems. Several applications areas are also discussed, including signal processing, system identification, optimal control, and machine learning. Today, most of the material on the optimization aspects of deep learning that is accessible for students at a Masters' level is focused on surface-level computer programming; deeper knowledge about the optimization methods and the trade-offs that are behind these methods is not provided. The objective of this book is to make this scattered knowledge, currently mainly available in publications in academic journals, accessible for Masters' students in a coherent way"-- |c Provided by publisher. | ||
| 588 | |a Description based on online resource; title from digital title page (viewed on May 25, 2023). | ||
| 505 | 0 | |a Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xvi -- 1 Introduction 3 -- 2 Linear Algebra 9 -- 2.1 Vectors and Matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 -- 2.2 Linear Maps and Subspaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 -- 2.3 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 -- 2.4 Algorithm Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 -- 2.5 Matrices with Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 -- 2.6 Quadratic Forms and Definiteness . . . . . . . . . . . . . . . . . . . . . . . . 30 -- 2.7 Spectral Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 -- 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 -- 2.9 Moore–Penrose Pseudoinverse . . . . . . . . . . . . . . . . . . . . . . . . . . 36 -- 2.10 Systems of Linear Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 -- 2.11 Factorization Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 -- 2.12 Vector and Matrix Calculus . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 -- 2.13 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 -- 3 Probability Theory 63 -- 3.1 Probability Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 -- 3.2 Conditional Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 -- 3.3 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 -- 3.4 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 -- 3.5 Conditional Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 -- 3.6 Expectations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 -- vii -- 3.7 Conditional Expectations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 -- 3.8 Convergence of Random Variables . . . . . . . . . . . . . . . . . . . . . . . . 80 -- 3.9 Random Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 -- 3.10 Markov Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 -- 3.11 Hidden Markov Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 -- 3.12 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 -- 3.13 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 -- 4 Optimization Theory 97 -- 4.1 Basic Concepts and Terminology . . . . . . . . . . . . . . . . . . . . . . . . . 97 -- 4.2 Convex Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 -- 4.3 Convex Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 -- 4.4 Subdifferentiability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 -- 4.5 Convex Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 131 -- 4.6 Duality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 -- 4.7 Optimality Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 -- 4.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 -- 5 Optimization Problems 147 -- 5.1 Least-Squares Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 -- 5.2 Quadratic Programs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 -- 5.3 Conic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 -- 5.4 Rank Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162 -- 5.5 Partially Separability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 -- 5.6 Multi-Parametric Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 173 -- 5.7 Stochastic Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175 -- 5.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178 -- 6 Optimization Methods 185 -- 6.1 Basic Principles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 186 -- viii -- 6.2 Gradient Descent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195 -- 6.3 Newton’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 -- 6.4 Variable Metric Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 -- 6.5 Proximal Gradient Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 -- 6.6 Sequential Convex Optimization . . . . . . . . . . . . . . . . . . . . . . . . . 221 -- 6.7 Methods for Nonlinear Least-Squares . . . . . . . . . . . . . . . . . . . . . . 223 -- 6.8 Stochastic Optimization Methods . . . . . . . . . . . . . . . . . . . . . . . . 228 -- 6.9 Coordinate Descent Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 -- 6.10 Interior-Point Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 244 -- 6.11 Augmented Lagrangian Methods . . . . . . . . . . . . . . . . . . . . . . . . . 254 -- 6.12 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260 -- 7 Calculus of Variations 273 -- 7.1 Extremum of Functionals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273 -- 7.2 The Pontryagin Maximum Principle . . . . . . . . . . . . . . . . . . . . . . . 280 -- 7.3 The Euler–Lagrange Equations . . . . . . . . . . . . . . . . . . . . . . . . . . 286 -- 7.4 Extensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289 -- 7.5 Numerical Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 294 -- 7.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305 -- 8 Dynamic Programming 321 -- 8.1 Finite Horizon Optimal Control . . . . . . . . . . . . . . . . . . . . . . . . . . 322 -- 8.2 Parametric Approximations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 -- 8.3 Infinite Horizon Optimal Control . . . . . . . . . . . . . . . . . . . . . . . . . 333 -- 8.4 Value Iterations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 -- 8.5 Policy Iterations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337 -- 8.6 Linear Programming Formulation . . . . . . . . . . . . . . . . . . . . . . . . . 343 -- 8.7 Model Predictive Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346 -- 8.8 Explicit MPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351 -- ix -- 8.9 Markov Decision Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 -- 8.10 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 364 -- 8.11 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 -- 9 Unsupervised Learning 381 -- 9.1 Chebyshev Bounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381 -- 9.2 Entropy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 383 -- 9.3 Prediction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 395 -- 9.4 The Viterbi Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 -- 9.5 Kalman Filter on Innovation Form . . . . . . . . . . . . . . . . . . . . . . . . 406 -- 9.6 Viterbi Decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411 -- 9.7 Graphical Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 -- 9.8 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . . . . . . . . 418 -- 9.9 Relative Entropy and Cross Entropy . . . . . . . . . . . . . . . . . . . . . . . 421 -- 9.10 The Expectation Maximization Algorithm . . . . . . . . . . . . . . . . . . . . 424 -- 9.11 Mixture Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 426 -- 9.12 Gibbs Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 430 -- 9.13 Boltzmann Machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433 -- 9.14 Principal Component Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 436 -- 9.15 Mutual Information . . . . | |
| 505 | 0 | |a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 441 -- 9.16 Cluster Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448 -- 9.17 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 451 -- 10 Supervised Learning 461 -- 10.1 Linear Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 461 -- 10.2 Regression in Hilbert Spaces . . . . . . . . . . . . . . . . . . . . . . . . . . . 466 -- 10.3 Gaussian Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 469 -- 10.4 Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 472 -- 10.5 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 475 -- x -- 10.6 Restricted Boltzmann Machine . . . . . . . . . . . . . . . . . . . . . . . . . . 481 -- 10.7 Artificial Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485 -- 10.8 Implicit Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 490 -- 10.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 495 -- 11 Reinforcement Learning 509 -- 11.1 Finite Horizon Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . 509 -- 11.2 Infinite Horizon Value I ... | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 630 | 0 | 0 | |a MATLAB. |
| 630 | 0 | 7 | |a MATLAB. |2 fast |0 (OCoLC)fst01365096 |
| 650 | 0 | |a System analysis |x Mathematics. | |
| 650 | 0 | |a Mathematical optimization. | |
| 650 | 0 | |a Machine learning |x Mathematics. | |
| 650 | 0 | |a Signal processing |x Mathematics. | |
| 650 | 7 | |a Mathematical optimization. |2 fast |0 (OCoLC)fst01012099 | |
| 650 | 7 | |a Signal processing |x Mathematics. |2 fast |0 (OCoLC)fst01118302 | |
| 700 | 1 | |a Andersen, Martin S., |e author. | |
| 776 | 0 | 8 | |i Print version: |a Hansson, Anders. |t Optimization for learning and control |b First edition. |d Hoboken, NJ, USA : Wiley, [2023] |z 9781119809135 |w (DLC) 2023002568 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781119809135/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a YBP Library Services |b YANK |n 305379419 | ||
| 994 | |a 92 |b IZTAP | ||