Run Lanczos for m iterations to get a good preconditioner.
View aliases
Main aliases
`tfp.experimental.fastgp.fast_gp.fast_log_det.partial_lanczos`, `tfp.experimental.fastgp.fast_gp.fast_log_det.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_gp.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_log_det.partial_lanczos`, `tfp.experimental.fastgp.fast_log_det.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.fast_gp.fast_log_det.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.fast_gp.fast_log_det.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.fast_gp.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.fast_log_det.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.fast_log_det.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.fast_mtgp.preconditioners.linalg.partial_lanczos`, `tfp.experimental.fastgp.linalg.partial_lanczos`, `tfp.experimental.fastgp.preconditioners.linalg.partial_lanczos`
Modules
mbcg module: Modified Batched Conjugate Gradients (mBCG) for JAX.
Classes
class Array: Array base class for JAX
Functions
gram_schmidt(...): Return a vector after applying the Gram-Schmidt process to it.
make_lanczos_preconditioner(...): Return a preconditioner as a linear operator.
my_tridiagonal_solve(...): Like jax.linalg.tridiagonal_solve, but works for all sizes.
partial_lanczos(...): Returns orthonormal Q and tridiagonal T such that A ~ Q^t T Q.
psd_solve_multishift(...): Solve (A - shift_k I) x_k = v for PSD A.
reorthogonalize(...)
tridiagonal_solve_multishift(...): Solve (T - shift_k I) x_k = v_norm e_1 for batch of sym. tridiagonal T.