ON SOLVING ENERGY-DEPENDENT PARTITIONED REAL SYMMETRIC MATRIX EIGENVALUE PROBLEM BY A PARALLEL GENETIC ALGORITHM

An energy-dependent partitioning scheme is explored for extracting a small number of eigenvalues of a real symmetric matrix with the help of a serial as well as parallel genetic algorithm (GA). The proposed method is tested on two matrices (up to 2000 × 2000) with an increasing number of processors in a master–slave architecture. A comparison is made with the Jacobi–Davidson method in serial mode as implemented in the JDQZ-package. Different partition sizes are used. Traditionally used Löwdin's method is also tested in both serial and parallel modes. The advantages and disadvantages of the parallel GA-based method in solving the partitioned eigenvalue problem are analyzed.