- Not-a-number (NaN) and other special floating point values
- Large inputs may lead to accumulator overflow
Sparse BLAS Routines
Sparse Solver Routines
Extended Eigensolver Routines
- Converges quickly in 2-3 iterations with very high accuracy
- Naturally captures all eigenvalue multiplicities
- No explicit orthogonalization procedure
- Can reuse the basis of pre-computed subspace as suitable initial guess for performing outer-refinement iterationsThis capability can also be used for solving a series of eigenvalue problems that are close one another.
- The number of internal iterations is independent of the size of the system and the number of eigenpairs in the search interval
- The inner linear systems can be solved either iteratively (even with modest relative residual error) or directly
Statistical FunctionsVector Statistics (VS) contains three sets of functions (see
- Pseudorandom, quasi-random, and non-deterministic random number generator subroutines implementing basic continuous and discrete distributions. To provide best performance, the VS subroutines use calls to highly optimized Basic Random Number Generators (BRNGs) and a set of vector mathematical functions.
- A wide variety of convolution and correlation operations.
- Initial statistical analysis of raw single and double precision multi-dimensional datasets.