Random Matrix Theory, Interacting Particle Systems and Integrable Systems
by Percy Deift, Peter Forrester (eds)
Publisher: Cambridge University Press 2014
Number of pages: 528
Random matrix theory is at the intersection of linear algebra, probability theory and integrable systems, and has a wide range of applications in physics, engineering, multivariate statistics and beyond. The book contains review articles and research contributions on all these topics, in addition to other core aspects of random matrix theory such as integrability and free probability theory.
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by R. Kochendörfer - Teubner
Basic methods and concepts are introduced. From the table of contents: Preliminaries; Determinants; Matrices; Vector spaces. Rank of a matrix; Linear Spaces; Hermitian/Quadratic forms; More about determinants and matrices; Similarity.
by Kaare Brandt Petersen, Michael Syskind Pedersen
The Matrix Cookbook is a free desktop reference on matrix identities, inequalities, approximations and relations useful for different fields such as machine learning, statistics, quantum mechanics, engeneering, chemistry.
by Shmuel Friedland - University of Illinois at Chicago
From the table of contents: Domains, Modules and Matrices; Canonical Forms for Similarity; Functions of Matrices and Analytic Similarity; Inner product spaces; Elements of Multilinear Algebra; Nonnegative matrices; Convexity.
by Steven J Cox - Rice University
Matrix theory is a language for representing and analyzing multivariable systems. These notes will demonstrate the role of matrices in the modeling of physical systems and the power of matrix theory in the analysis and synthesis of such systems.