http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Compressed Sensing Approach to Systems and Control
The ebook edition of this title is Open Access and freely available to read online. This book provides a comprehensive guide to compressed sensing-based techniques, focusing primarily on their application to systems and control.
Cover -- Compressed Sensing Approach to Systems and Control -- EURASIP-Now Publishers Open Access Book Series on Information and Learning Sciences -- Copyright Page -- Contents -- Preface -- Acknowledgements -- Notation -- 1: Introduction -- 1.1 Occam's Razor -- 1.2 Optimization with ℓ1 Norm -- 1.2.1 Signal reconstruction -- 1.2.2 Geophysics -- 1.2.3 Neural networks -- 1.2.4 Statistics -- 1.2.5 Signal processing -- 1.3 Sparsity Methods for Systems and Control -- 1.3.1 Minimum-fuel control and L1 optimization -- 1.3.2 Maximum hands-off control -- 1.3.3 Discrete-valued control
1.3.4 Robust control and rank minimization -- 1.3.5 Resource-aware control for networked control systems -- I: Compressed Sensing in Finite-dimensional Spaces -- 2: What is Sparsity? -- 2.1 Redundant Dictionary -- 2.2 Underdetermined Systems -- 2.3 The ℓ0 Norm -- 2.4 Group Testing -- 2.5 Exhaustive Search -- 2.6 Advanced Topic: Sparse Representation for Functions -- 2.7 Further Readings -- 3: Sparse Optimization -- 3.1 Least Squares and Regularization -- 3.1.1 Underdetermined system and minimum ℓ2-norm solution -- 3.1.2 Regression and least squares -- 3.1.3 Ridge regularization
3.1.4 Weighted ridge regression -- 3.1.5 Summary of ℓ2-norm optimization -- 3.2 Sparse Polynomial and ℓ1-norm Optimization -- 3.3 Python Examples -- 3.3.1 Sparse polynomial curve fitting -- 3.3.2 Group testing -- 3.4 Further Readings -- 4: Algorithms for Convex Optimization -- 4.1 Basics of Convex Optimization -- 4.2 Proximal Operators -- 4.2.1 Definition -- 4.2.2 Proximal algorithm -- 4.2.3 Proximal operator for quadratic function -- 4.2.4 Proximal operator for indicator function -- 4.2.5 Proximal operator for ℓ1 norm -- 4.3 Proximal Splitting Methods for ℓ1 Optimization
4.3.1 Douglas-Rachford splitting algorithm -- 4.3.2 Dykstra-like splitting algorithm -- 4.4 Proximal Gradient Methods for ℓ1 Regularization -- 4.4.1 Algorithm -- 4.4.2 Convergence analysis and acceleration -- 4.4.3 Noisy group testing by FISTA -- 4.5 Generalized LASSO and ADMM -- 4.5.1 Algorithm -- 4.5.2 Total variation denoising -- 4.6 Further Readings -- 5: Greedy Algorithms -- 5.1 ℓ0 Optimization -- 5.2 Orthogonal Matching Pursuit -- 5.2.1 Matching pursuit (MP) -- 5.2.2 Orthogonal matching pursuit (OMP) -- 5.3 Thresholding Algorithms -- 5.3.1 Iterative hard-thresholding algorithm (IHT)
5.3.2 Iterative s-sparse algorithm -- 5.3.3 Compressive sampling matching pursuit (CoSaMP) -- 5.4 Numerical Example -- 5.5 Further Readings -- 5.6 Python Programs -- 6: Distributed Optimization -- 6.1 Network Model and Algebraic Graph Theory -- 6.2 Consensus Algorithm -- 6.3 Distributed Optimization -- 6.3.1 Distributed gradient descent -- 6.3.2 Distributed ADMM algorithm -- 6.3.3 Distributed least squares -- 6.3.4 Distributed sparse regularization -- 6.4 Further Readings -- 6.5 Python Programs -- 6.5.1 Example 6.3 -- 6.5.2 Example 6.4 -- 6.5.3 Example 6.5 -- 6.5.4 Example 6.6
Description based upon print version of record.