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The numerical solution of partial differential equations frequently requires the solution of large and sparse linear systems. Using generic programming techniques like in C++ one can create solver libraries that allow efficient realization of "fine grained interfaces", i. e. with functions consisting only of a few lines, like access to individual matrix entries. This prevents code replication and allows programmers to work more efficiently. In this paper we present the "Iterative Solver Template Library" (ISTL) which is part of the "Distributed and Unified Numerics Environment" (DUNE). It applies generic programming in C++ to the domain of iterative solvers of linear systems stemming from finite element discretizations. Those discretizations exhibit a lot of structure. Our matrix and vector interface supports a block recursive structure. I. E. each sparse matrix entry can be a sparse or a small dense matrix itself. Based on this interface we present efficient solvers that use the recursive block structure via template metaprogramming.

SUMMARY We present a new algebraic multigrid (AMG) algorithm for the solution of linear systems arising from discontinuous Galerkin (DG) discretizations of heterogeneous elliptic problems. The algorithm is based on the idea of subspace corrections, and the first coarse level space is the subspace spanned by continuous linear basis functions. The linear system associated with this space is constructed algebraically using a Galerkin approach with the natural embedding as the prolongation operator. This embedding operator needs to be provided, which means that the approach is not fully algebraic. For the construction of the linear systems on the subsequent coarser levels, non‐smoothed aggregation AMG techniques are used. In a series of numerical experiments, we establish for the first time the efficiency and robustness of an AMG method for various symmetric and non‐symmetric interior penalty DG methods (including the higher‐order cases) on problems with complicated, high contrast jumps in the coefficients. The solver is robust with respect to an increase in the polynomial degree of the DG approximation space (at least up to degree 6), computationally efficient, and affected only mildly by the coefficient jumps and by the mesh size h (i.e., number of iterations = O(log h−1)). Copyright © 2012 John Wiley & Sons, Ltd.

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