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Xian-Da Zhang. Matrix Analysis And Applications 2017
Linear algebra is a vast field of fundamental importance in most areas of pure (and applied) mathematics, while matrices are a key tool for the researchers, scientists, engineers and graduate students majoring in the science and engineering disciplines. From the viewpoint of applications, matrix analysis provides a powerful mathematical modeling and computational framework for posing and solving important scientific and engineering problems. It is no exaggeration to say that matrix analysis is one of the most creative and flexible mathematical tools and that it plays an irreplaceable role in physics, mechanics, signal and information processing, wireless communications, machine learning, computer vision, automatic control, system engineering, aerospace, bioinformatics, medical image processing and many other disciplines, and it effectively supports research in them all. At the same time, novel applications in these disciplines have spawned a number of new results and methods of matrix analysis, such as quadratic eigenvalue problems, joint diagonalization, sparse representation and compressed sensing, matrix completion, nonnegative matrix factorization, tensor analysis and so on
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