As a part of other work I need to solve relatively large (~1E5x1E5) and sparse (~100 non-zero elements in each raw in few blocks) hermitian eigensystems. Usually only few eigenvalues+vectors are needed, but with high precision. Currently I am using Arpack (Arnoldi with shift-inverse when precision is preferable or spectrum folding when size is important). As an option plan to use TRLan (thick restart Lanczos) and try Chebyshev filtering instead of spectrum folding.Probably, newer methods exist for this purpose?
Sparse hermitian eigensystems: are there better techniques than Arpack or TRLan?
linear algebra;eigensystem;matrices;sparse