Using a modern matrix-based approach, this rigorous second course in
linear algebra helps upper-level undergraduates in mathematics, data
science, and the physical sciences transition from basic theory to
advanced topics and applications. Its clarity of exposition together
with many illustrations, 900+ exercises, and 350 conceptual and
numerical examples aid the student's understanding. Concise chapters
promote a focused progression through essential ideas. Topics are
derived and discussed in detail, including the singular value
decomposition, Jordan canonical form, spectral theorem, QR
factorization, normal matrices, Hermitian matrices, and positive
definite matrices. Each chapter ends with a bullet list summarizing
important concepts. New to this edition are chapters on matrix norms and
positive matrices, many new sections on topics including interpolation
and LU factorization, 300+ more problems, many new examples, and
color-enhanced figures. Prerequisites include a first course in linear
algebra and basic calculus sequence. Instructor's resources are
available.