PseudoInput¶

class itergp.methods.PseudoInput(pseudo_inputs)¶

Pseudo-input approximation method.

Parameters: pseudo_inputs – Pseudo inputs, also known as inducing points, at which kernel function actions are centered.

Attributes Summary

Pseudo inputs, at which kernel function actions are centered.

Methods Summary

`solve`(prior, problem[, rng])	Solve the linear system.
`solve_iterator`(prior, problem[, rng])	Generator implementing the solver iteration.

Attributes Documentation

pseudo_inputs¶: Pseudo inputs, at which kernel function actions are centered.

Methods Documentation

solve(prior, problem, rng=None)¶

Solve the linear system.

Parameters

prior (LinearSystemBelief) – Prior belief about the quantities of interest \((x, A, A^{-1}, b)\) of the linear system.
problem (LinearSystem) – Linear system to be solved.
rng (Optional[Generator]) – Random number generator.

Returns

belief – Posterior belief \((\mathsf{x}, \mathsf{A}, \mathsf{H}, \mathsf{b})\) over the solution \(x\), the system matrix \(A\), its (pseudo-)inverse \(H=A^\dagger\) and the right hand side \(b\).
solver_state – Final state of the solver.

Return type

Tuple[LinearSystemBelief, LinearSolverState]

solve_iterator(prior, problem, rng=None)¶

Generator implementing the solver iteration.

This function allows stepping through the solver iteration one step at a time and exposes the internal solver state.

Parameters

prior (LinearSystemBelief) – Prior belief about the quantities of interest \((x, A, A^{-1}, b)\) of the linear system.
problem (LinearSystem) – Linear system to be solved.
rng (Optional[Generator]) – Random number generator.

Yields

solver_state – State of the probabilistic linear solver.

Return type

Generator[LinearSolverState, None, None]