Seeking sparse solutions of underdetermined linear systems is required in many areas of engineering and science such as signal and image processing. The efficient sparse representation becomes central in various big or high-dimensional data processing, yielding fruitful theoretical and realistic results in these fields. The mathematical optimization plays a fundamentally important role in the development of these results and acts as the mainstream numerical algorithms for the sparsity-seeking problems arising from big-data processing, compressed sensing, statistical learning, computer vision, and so on. This has attracted the interest of many researchers at the interface of engineering, mathematics and computer science.
Sparse Optimization Theory and Methods presents the state of the art in theory and algorithms for signal recovery under the sparsity assumption. The up-to-date uniqueness conditions for the sparsest solution of underdertemined linear systems are described. The results for sparse signal recovery under the matrix property called range space property (RSP) are introduced, which is a deep and mild condition for the sparse signal to be recovered by convex optimization methods. This framework is generalized to 1-bit compressed sensing, leading to a novel sign recovery theory in this area. Two efficient sparsity-seeking algorithms, reweighted l1-minimization in primal space and the algorithm based on complementary slackness property, are presented. The theoretical efficiency of these algorithms is rigorously analysed in this book. Under the RSP assumption, the author also provides a novel and unified stability analysis for several popular optimization methods for sparse signal recovery, including l1-mininization, Dantzig selector and LASSO. This book incorporates recent development and the authors latest research in the field that have not appeared in other books.
Preface
Uniqueness of the Sparsest Solution of Linear Systems
Introduction
Spark
Uniqueness via Mutual Coherence
Improved Uniqueness Criteria via Coherence Rank
Babel Function and Sub-Babel Function
Notes
Uniqueness of Solutions to `1-Minimization Problems
Strict Complementary Slackness Property (SCSP)
Least `1-Norm Nonnegative Solution
Least `1-Norm Points in Polyhedra
Notes
Equivalence of `0- and `1-Minimization
Equivalence and Strong Equivalence
Standard `0- and `1-Minimization Problems
Problems with Nonnegativity Constraints
Application to Linear Programming
Equivalence of `0-Problem and Weighted `1-Problem
Sparse Vector Recovery
Sparse Nonnegative Vector Recovery
Notes
Bit Compressed Sensing
Introduction
Sign Measurements and Recovery Criteria
Relaxation Models
Consistency Condition
Reformulation of 1-Bit Compressed Sensing
Nonuniform Sign Recovery
Uniform Sign Recovery
Notes
Stability of Linear Sparse Optimization Methods
Introduction
Hoffmans Error Bound for Linear Systems
Weak RSP of Order k of AT
Stability of Standard `1-Minimization
Linear Dantzig Selector
Special Cases
Notes
Stability of Nonlinear Sparse Optimization Methods
Introduction
Orthogonal Projection Operator
Polytope Approximation of Unit Balls
A Necessary Condition for Stability
`1-Minimization with `2-Norm Constraints
Nonlinear Dantzig Selector
The LASSO Problem
Summary
Notes
Reweighted `1-Algorithms
Merit Function for Sparsity
Reweighted `1-Methods
Numerical Experiments
Theoretical Analysis
Notes
Sparsity via Dual Density
Introduction
`0-Minimization with Nonnegativity Constraints
DDRW for Standard `0-Minimization
Sparsity Enhancement for Weighted `1-Minimizers
Notes
References
(0 Comentarios)
