kaczmarz | R Documentation |
Uses the Kaczmarz method to solve a system of the type Dx = R, where D is the matrix of dummies created from a list of factors.
kaczmarz( fl, R, eps = getOption("lfe.eps"), init = NULL, threads = getOption("lfe.threads") )
fl |
A list of arbitrary factors of the same length |
R |
numeric. A vector, matrix or list of such of the same length as the factors |
eps |
a tolerance for the method |
init |
numeric. A vector to use as initial value for the Kaczmarz iterations. The algorithm converges to the solution closest to this |
threads |
integer. The number of threads to use when |
A vector x
of length equal to the sum of the number of levels
of the factors in fl
, which solves the system Dx=R. If the
system is inconsistent, the algorithm may not converge, it will give a
warning and return something which may or may not be close to a solution. By
setting eps=0
, maximum accuracy (with convergence warning) will be
achieved.
This function is used by getfe()
, it's quite specialized,
but it might be useful for other purposes too.
In case of convergence problems, setting options(lfe.usecg=TRUE)
will
cause the kaczmarz() function to dispatch to the more general conjugate
gradient method of cgsolve()
. This may or may not be faster.
cgsolve()
## create factors f1 <- factor(sample(24000, 100000, replace = TRUE)) f2 <- factor(sample(20000, length(f1), replace = TRUE)) f3 <- factor(sample(10000, length(f1), replace = TRUE)) f4 <- factor(sample(8000, length(f1), replace = TRUE)) ## the matrix of dummies D <- makeDmatrix(list(f1, f2, f3, f4)) dim(D) ## an x truex <- runif(ncol(D)) ## and the right hand side R <- as.vector(D %*% truex) ## solve it sol <- kaczmarz(list(f1, f2, f3, f4), R) ## verify that the solution solves the system Dx = R sqrt(sum((D %*% sol - R)^2)) ## but the solution is not equal to the true x, because the system is ## underdetermined sqrt(sum((sol - truex)^2)) ## moreover, the solution from kaczmarz has smaller norm sqrt(sum(sol^2)) < sqrt(sum(truex^2))
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