紹介
With a substantial amount of new material, the Handbook of Linear Algebra, Second Edition provides comprehensive coverage of linear algebra concepts, applications, and computational software packages in an easy-to-use format. It guides you from the very elementary aspects of the subject to the frontiers of current research. Along with revisions and updates throughout, the second edition of this bestseller includes 20 new chapters.
New to the Second Edition Separate chapters on Schur complements, additional types of canonical forms, tensors, matrix polynomials, matrix equations, special types of matrices, generalized inverses, matrices over finite fields, invariant subspaces, representations of quivers, and spectral sets New chapters on combinatorial matrix theory topics, such as tournaments, the minimum rank problem, and spectral graph theory, as well as numerical linear algebra topics, including algorithms for structured matrix computations, stability of structured matrix computations, and nonlinear eigenvalue problems More chapters on applications of linear algebra, including epidemiology and quantum error correction New chapter on using the free and open source software system Sage for linear algebra Additional sections in the chapters on sign pattern matrices and applications to geometry Conjectures and open problems in most chapters on advanced topics Highly praised as a valuable resource for anyone who uses linear algebra, the first edition covered virtually all aspects of linear algebra and its applications.
This edition continues to encompass the fundamentals of linear algebra, combinatorial and numerical linear algebra, and applications of linear algebra to various disciplines while also covering up-to-date software packages for linear algebra computations.
目次
Basic Linear Algebra. Matrices with Special Properties. Advanced Linear Algebra. Topics in Advanced Linear Algebra. Matrices and Graphs. Topics in Combinatorial Matrix Theory. Numerical Methods for Linear Systems. Numerical Methods for Eigenvalues. Computational Linear Algebra. Applications to Optimization. Applications to Probability and Statistics. Applications to Analysis. Applications to Physical and Biological Sciences. Applications to Computer Science. Applications to Geometry. Applications to Algebra. Interactive Software for Linear Algebra. Packages of Subroutines for Linear Algebra.
