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 author's latest research in the field that have not appeared in other books.

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 author's latest research in the field that have not appeared in other books.

Drawing on original research I conducted in the late 1980s, the book argues for a critical approach to the study of children and television. It begins with critical reappraisals of previous empiricist and interpretative studies to set the ground for a different theoretical inquiry which links biography with history. The situated activity of children's television viewing therefore has to be related to the broader historical and cultural formations in post-Mao China. By way of a methodological pluralism of questionnaire survey, in-depth interviews and observation, the book provides the reader with a thorough critical analysis of the rise of the new commercial ethic in Chinese society in general, and in the sector of media and communications in particular, at the very historical turning point of the late 1980s. Soon after that, Deng Xiaoping made his significant tour to south China, reckoning a big step forward towards further liberalization and started to form a brave new world in China ever since.

Drawing on original research I conducted in the late 1980s, the book argues for a critical approach to the study of children and television. It begins with critical reappraisals of previous empiricist and interpretative studies to set the ground for a different theoretical inquiry which links biography with history. The situated activity of children's television viewing therefore has to be related to the broader historical and cultural formations in post-Mao China. By way of a methodological pluralism of questionnaire survey, in-depth interviews and observation, the book provides the reader with a thorough critical analysis of the rise of the new commercial ethic in Chinese society in general, and in the sector of media and communications in particular, at the very historical turning point of the late 1980s. Soon after that, Deng Xiaoping made his significant tour to south China, reckoning a big step forward towards further liberalization and started to form a brave new world in China ever since. "

The QL&SC 2012 is a major symposium for scientists, and practitioners all around the world to present their latest researches, results, ideas, developments and applications in such areas as quantitative logic, many-valued logic, fuzzy logic, quantification of software, artificial intelligence, fuzzy sets and systems and soft computing.This invaluable book provides a broad introduction to the fuzzy reasoning and soft computing. It is certain one should not go too far in approximation and optimization, and a certain degree must be kept in mind. This is the essential idea of quantitative logic and soft computing.The explanations in the book are complete to provide the necessary background material needed to go further into the subject and explore the research literature. It is suitable reading for graduate students. It provides a platform for mutual exchanges from top experts and scholars around the world in this field.

This book is devoted to the optimum design of the DCT in a hybrid AC/DC microgrid, which takes into account not only the influence of different inductors/capacitors values, but also numerous design goals (i.e., VCG, efficiency, stability and so on). This book examines the DCT's design problem in detail. It begins by reviewing existing DCTs in, the hybrid AC/DC microgrid and their design problems. Following that, this book proposes a family of DCT optimization design approaches to ensure that the designed DCT has good power transmission and voltage regulation ability in the hybrid AC/DC microgrid, even when the actual inductors/capacitors values fluctuate with practical power and temperature. Following that, this book provides a family of multi-objective optimization design methodologies for the DCT to guarantee that it concurrently achieves the requirements of VCG, efficiency, and system stability. This book also covers how to control the DCT in a hybrid AC/DC microgrid optimally and generically.