This chapter studies finite difference methods for elliptic problems. It begins with a rather lengthy and general discussion of grid domains, grid functions, finite difference operators, and their consistency. We then introduce the notion of stability of a finite difference scheme and Lax’s principle: a consistent and stable scheme is convergent. Then we apply all these notions to elliptic operators in one and two dimensions, with the main focus being the Laplacian. We show the discrete maximum principle, energy arguments and how these can be used to attain stability and convergence in various norms. For more general operators we introduce the notions of homogeneous schemes and upwinding. For operators in divergence form we provide an analysis via energy arguments. For non divergence form operators we analyze the monotonicity and comparison principles of the arising schemes.

In this chapter we study finite difference schemes for parabolic partial differential equations. The notions of conditional and unconditional stability, and CFL condition are introduced to analyze the classical schemes for the heat equation. Different techniques, like maximum principles and energy arguments are presented to obtain stability in different norms. Then, we turn to the study of the pure initial value problem, the grand goal being to discuss the von Neumann stability analysis. To accomplish this we introduce the notions of Fourier-Z transform of grid functions and the symbol of a finite difference scheme. This allows us to state the von Neumann stability condition and prove that it is necessary and sufficient for stability. These notions are also used to present a covergence analysis that is somewhat different than the one presented in previous sections.

In this chapter we present and analyze the stability of Galerkin methods for ordinary differential equations. We present the discontinuous Galerkin method for problems with coercive operators, discuss its stability and convergence. Then, for problems with monotone operators, the continuous Petrov–Galerkin method is introduced and analyzed. We show how, in simple scenarios these methods reduced to ones that have been discussed in previous chapters.