num_pages: 510
description: This textbook emphasizes the interplay between algebra and geometry to motivate the study of advanced linear algebra techniques. Matrices and linear transformations are presented as two sides of the same coin with their connection motivating inquiry throughout the book. Building on a first course in linear algebra this book offers readers a deeper understanding of abstract structures matrix decompositions multilinearity and tensors. Concepts draw on concrete examples throughout offering accessible pathways to advanced techniques.Beginning with a study of vector spaces that includes coordinates isomorphisms orthogonality and projections the book goes on to focus on matrix decompositions. Numerous decompositions are explored including the Shur spectral singular value and Jordan decompositions. In each case the author ties the new technique back to familiar ones to create a coherent set of tools. Tensors and multilinearity complete the book with a study of the Kronecker product multilinear transformations and tensor products. Throughout Extra Topic sections augment the core content with a wide range of ideas and applications from the QR and Cholesky decompositions to matrix-valued linear maps and semidefinite programming. Exercises of all levels accompany each section.Advanced Linear and Matrix Algebra offers students of mathematics data analysis and beyond the essential tools and concepts needed for further study. The engaging color presentation and frequent marginal notes showcase the authors visual approach. A first course in proof-based linear algebra is assumed. An ideal preparation can be found in the authors companion volume Introduction to Linear and Matrix Algebra.
language: English
title: Advanced Linear and Matrix Algebra
publisher: Springer