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Robust software with error bounds for computing the generalized Schur decomposition of an arbitrary matrix pencil A – λB (regular or singular) is presented. The decomposition is a generalization of the Schur canonical form of A – λI to matrix pencils and reveals the Kronecker structure of a singular pencil. Since computing the Kronecker structure of a singular pencil is a potentially ill-posed problem, it is important to be able to compute rigorous and reliable error bounds for the computed features. The error bounds rely on perturbation theory for reducing subspaces and generalized eigenvalues of singular matrix pencils. The first part of this two-part paper presents the theory and algorithms for the decomposition and its error bounds, while the second part describes the computed generalized Schur decomposition and the software, and presents applications and an example of its use.

Matrix computations are both fundamental and ubiquitous in computational science and its vast application areas. Along with the development of more advanced computer systems with complex memory hierarchies, there is a continuing demand for new algorithms and library software that efficiently utilize and adapt to new architecture features. This article reviews and details some of the recent advances made by applying the paradigm of recursion to dense matrix computations on today's memory-tiered computer systems. Recursion allows for efficient utilization of a memory hierarchy and generalizes existing fixed blocking by introducing automatic variable blocking that has the potential of matching every level of a deep memory hierarchy. Novel recursive blocked algorithms offer new ways to compute factorizations such as Cholesky and QR and to solve matrix equations. In fact, the whole gamut of existing dense linear algebra factorization is beginning to be reexamined in view of the recursive paradigm. Use of recursion has led to using new hybrid data structures and optimized superscalar kernels. The results we survey include new algorithms and library software implementations for level 3 kernels, matrix factorizations, and the solution of general systems of linear equations and several common matrix equations. The software implementations we survey are robust and show impressive performance on today's high performance computing systems.

Robust software with error bounds for computing the generalized Schur decomposition of an arbitrary matrix pencil A – λB (regular or singular) is presented. The decomposition is a generalization of the Schur canonical form of A – λI to matrix pencils and reveals the Kronecker structure of a singular pencil. The second part of this two-part paper describes the computed generalized Schur decomposition in more detail and the software, and presents applications and an example of its use. Background theory and algorithms for the decomposition and its error bounds are presented in Part I of this paper.