  Efficient and stable algorithms for the calculation of spectral quantities
and correlation functions are some of the key tools in computational condensed
matter physics. In this article we review basic properties and recent
developments of Chebyshev expansion based algorithms and the Kernel Polynomial
Method. Characterized by a resource consumption that scales linearly with the
problem dimension these methods enjoyed growing popularity over the last decade
and found broad application not only in physics. Representative examples from
the fields of disordered systems, strongly correlated electrons,
electron-phonon interaction, and quantum spin systems we discuss in detail. In
addition, we illustrate how the Kernel Polynomial Method is successfully
embedded into other numerical techniques, such as Cluster Perturbation Theory
or Monte Carlo simulation.
