For a numerical evolution, numerical stability means that perturbations remain controlled over a fixed finite interval by a bound independent of sufficiently small discretization parameters. With perturbations to starting data and added step defects , a typical estimate iswhere is independent of . For a linear homogeneous recurrence, a uniform bound on powers of its update matrix supplies this control. von Neumann stability analysis and the energy method provide complementary tools.
Finite-interval numerical stability differs from absolute stability at a fixed step size. Applying the Euler method to multiplies a perturbation by each step. At this is , so perturbations shrink; at it is , so they alternate and grow. Nevertheless the Euler method converges as for this problem: failure at a coarse step does not contradict the convergence theorem. Absolute stability asks whether a selected step preserves boundedness of the Dahlquist test equation over arbitrarily many steps. MIT's analysis uses strict decay, , rather than the boundary-inclusive boundedness convention.[ref]
https://ocw.mit.edu/courses/18-330-introduction-to-numerical-analysis-spring-2012/a9d2bd9be098f0ada172af40379a17cc_MIT18_330S12_Chapter5.pdf. Laurent Demanet, MIT 18.330, Chapter 5, Example 14, 2012
In the contraction convention for numerical evolution, practical stability means non-growth of the chosen norm for every datum and every permitted time level. This is stronger than finite-time mesh-uniform stability allowing a bound . For a scalar translation-invariant one-step scheme on the infinite grid, a Fourier amplification symbol with gives this contraction by the Parseval identity. If on a frequency set of positive measure, choose a nonzero square-integrable transform supported there to obtain . The constant may depend on the mesh. Centered pure advection with forward time has on the set . The distinction from finite-time stability is quantified by centered-advection refinement-dependent stability.
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