CG: Solve Ax=b by preconditioned conjugate gradients
In mcmcsae: Markov Chain Monte Carlo Small Area Estimation

View source: R/conjugate_gradients.R

CG	R Documentation

Solve Ax=b by preconditioned conjugate gradients

Description

Solve Ax=b by preconditioned conjugate gradients

Usage

CG(b, env, x = NULL, max.it = NULL, e = NULL, verbose = FALSE, ...)

Arguments

`b`	right hand side vector.
`env`	environment containing at least a function `A_times` that computes the matrix-vector product Ax for some input vector x, and a function `M_solve` that computes M^-1 x for some preconditioner matrix M.
`x`	start value for the conjugate gradient algorithm.
`max.it`	maximum number of iterations.
`e`	total squared error stop criterion.
`verbose`	whether progress information is shown.
`...`	any parameters passed to `A_times` and `M_solve`.

Value

The (approximated) solution to Ax=b.

References

M.R. Hestenes and E. Stiefel (1952). Methods of conjugate gradients for solving linear systems. Journal of Research of the National Bureau of Standards 49(6), 409-436.

mcmcsae documentation built on Oct. 11, 2023, 1:06 a.m.