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In this paper we derive a framework for multiscale approximation of elliptic problems on standard and mixed form. The method presented is based on a splitting into coarse and fine scales together with a systematic technique for approximation of the fine scale part, based on the solution of decoupled localized subgrid problems. The fine scale approximation is then used to modify the coarse scale equations. A key feature of the method is that symmetry in the bilinear form is preserved in the discrete system. Other key features are a posteriori error bounds and adaptive algorithms based on these bounds. The adaptive algorithms are used for automatic tuning of the method parameters. In the last part of the paper we present numerical examples where we apply the framework to a problem in oil reservoir simulation. [ABSTRACT FROM AUTHOR]

In (SIAM J. Matrix Anal. Appl., 23 (2002), pp. 706–727, we developed numerical algorithms for computing sparse low-rank approximations of matrices, and we also provided a detailed error analysis of the proposed algorithms together with some numerical experiments. The low-rank approximations are constructed in a certain factored form with the degree of sparsity of the factors controlled by some user-specified parameters. In this paper, we cast the sparse low-rank approximation problem in the framework of penalized optimization problems. We discuss various approximation schemes for the penalized optimization problem which are more amenable to numerical computations. We also include some analysis to show the relations between the original optimization problem and the reduced one. We then develop a globally convergent discrete Newton-like iterative method for solving the approximate penalized optimization problems. We also compare the reconstruction errors of the sparse low-rank approximations computed by our new methods with those obtained using the methods in the earlier paper and several other existing methods for computing sparse low-rank approximations. Numerical examples show that the penalized methods are more robust and produce approximations with factors which have fewer columns and are sparser. [ABSTRACT FROM AUTHOR]