Symmetric Matrix Eigenvalue Techniques
Symmetric Matrix Eigenvalue Techniques
The article describes symmetric matrix eigenvalue techniques: basic methods (power method, inverse iteration, orthogonal iteration and QR iteration), tridiagonalization and implicitly shifted QR method, divide-and-conquer method, bisection and inverse iteration, the method of multiple relatively robust representations, Jacobi method and Lanczos method. For each method we give algorithms, basic theoretical facts and examples. The article concludes by giving comparison of the methods which are implemented in LAPack.
bisection method, symmetric matrix, inverse iteration method, method of relatively robust representations, Lanczos method, eigenvalues, tridiagonalization, eigenvectors, divide and conquer method, QR iteration, Jacobi method, power method
bisection method, symmetric matrix, inverse iteration method, method of relatively robust representations, Lanczos method, eigenvalues, tridiagonalization, eigenvectors, divide and conquer method, QR iteration, Jacobi method, power method
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).0 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!