Accurate and efficient determination of higher roots in diagonalization of large matrices based in function restricted optimization algorithms

The problem of large‐scale matrix diagonalization is analyzed in the context of normal function optimization techniques with particular emphasis on the problem of obtaining high roots. New methods based on function restricted optimization algorithms are presented. The efficiency of these methods is illustrated for lowest and higher and degenerate roots of selected matrices. The diagonalization process is commonly carried out in a subspace, and involves a sort of optimization process, and the dimension of this subspace increases at each iteration. In addition, the success of a diagonalization method in obtaining a desired root strongly depends on the particular optimization procedure chosen. In this work, a rational function optimization procedure is presented that permits obtaining the lowest and higher eigenpairs in an efficient way. Update Hessian matrices formulae, routinely used in normal function optimization problems, are explored in the framework of diagonalization techniques. Finally, a diagonalization method with a fixed subspace dimension during the iterative process is presented. Some examples focused in lowest, higher and degenerate eigenpairs are discussed. © 2000 John Wiley & Sons, Inc. J Comput Chem 21: 1375–1386, 2000