"Shifting." . . "Algorithms." . . . . "Operational effectiveness." . . "Computer Systems Management and Standards." . . "Computer programs." . . "Linear systems." . . "Eigenvalues." . . "Institute for Computer Applications in Science and Engineering." . . "Estimates." . . "Iterations." . . "Parallel processing." . . "Computer programming." . . "Computer Programming and Software." . . "Scale." . . . . "Abstract: \"Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speed-up is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented.\""@en . . . . . . "Using parallel banded linear system solvers in generalized eigenvalue problems"@en . . "Using Parallel Banded Linear System Solvers in Generalized Eigenvalue Problems"@en . . . . . . "Using parallel banded linear system solvers in generalized Eigenvalue problems"@en . . . . . . . . . . . . . . . . . "Subspace iteration is a reliable and cost effective method for solving positive definite banded symmetric generalized eigenproblems, especially in the case of large scale problems. This paper discusses an algorithm that makes use of two parallel banded solvers in subspace iteration. A shift is introduced to decompose the banded linear systems into relatively independent subsystems and to accelerate the iterations. With this shift, an eigenproblem is mapped efficiently into the memories of a multiprocessor and a high speed-up is obtained for parallel implementations. An optimal shift is a shift that balances total computation and communication costs. Under certain conditions, we show how to estimate an optimal shift analytically using the decay rate for the inverse of a banded matrix, and how to improve this estimate. Computational results on iPSC/2 and iPSC/860 multiprocessors are presented."@en . .