Linearity, as first encountered in the algebra of vectors and matrices, provides
the foundation for many other branches of mathematics. This course provides
an introduction to two directions that directly follow from the ideas of linear algebra.
The first is the classical extension to function spaces, and is presented within
the specific context of Fourier analysis. Expansions upon this development
naturally lead to the theories of orthogonal polynomials, Hilbert spaces and
linear operators. These ideas will be investigated mainly from the perspectives
of classical real analysis, but will also include concrete illustrations using
elementary numerical computing. The second is the modern development of
large-matrix algorithms, many of which are based upon well-established
principles of linear operator theory, and are now made feasible by advances in
computational speed and memory. Examples include the fast Fourier transform,
the singular value decomposition, and Krylov subspace methods.
In this fourth-year course, the lectures will invoke aspects of both
rigorous analysis and elementary numerical computing. Computer visualization will be an important
accompaniment to the lectures and assigned work. The rudiments of
numerical computing and graphics will be introduced through the use
and modification of downloadable Matlab scripts.
Prerequisites are linear algebra (MATH 232) and analysis (MATH 320).
Background familiarity with elementary differential equations (MATH 310 &
314), complex variables (MATH 322), numerical analysis (MATH 316) and
Matlab computing are advantageous, but not essential. Consult instructor
for more specific information.