In order to solve all or some eigenvalues lied in a cluster, we propose a weighted block Golub-Kahan-Lanczos algorithm for the linear response eigenvalue problem. Error bounds of the approximations to an eigenvalue cluster, as well as their corresponding eigenspace, are established and show the advantages. A practical thick-restart strategy is applied to the block algorithm to eliminate the increasing computational and memory costs, and the numerical instability. Numerical examples illustrate the effectiveness of our new algorithms.
Abstract
In the work a single-machine scheduling problem is being considered, in which all tasks have a fixed availability (release) and delivery time. In the analyzed variant no-idle time is allowed on a machine. The purpose of optimization is to determine such order of tasks that minimizes the makespan, i.e. the time of execution of all the tasks. There is also a number of properties of the problem presented, in particular there are formulated block eliminating properties for no-idle constraint. There was an exact B&B algorithm based on the block properties proposed.
Weighted Block Golub-Kahan-Lanczos Algorithms for Linear Response Eigenvalue Problem
