R Under development (unstable) (2025-08-17 r88631 ucrt) -- "Unsuffered Consequences"
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> ## compute PCA of iris data set with svd() and sparsesvd()
> library(sparsesvd)
> library(Matrix)
> 
> data(iris)
> M <- scale(as.matrix(iris[, 1:4]), scale=FALSE)
> Ms <- Matrix(M) # not sparse, but a dMatrix
> 
> res1 <- svd(M)
> res2 <- sparsesvd(Ms)
> 
> ## check that eigenvalues are the same
> print(res2$d, digits=3)
[1] 25.10  6.01  3.41  1.88
> stopifnot(all.equal(res1$d, res2$d, tolerance=1e-12))
> 
> ## these should be diagonal unit matrices
> UtU <- abs(crossprod(res2$u, res1$u)) # diagonal entries may be 1 or -1
> VtV <- abs(crossprod(res2$v, res1$v)) # (because sign of eigenvectors is arbitrary)
> I1 <- diag(1, length(res1$d))
> 
> print(round(UtU, 12))
     [,1] [,2] [,3] [,4]
[1,]    1    0    0    0
[2,]    0    1    0    0
[3,]    0    0    1    0
[4,]    0    0    0    1
> stopifnot(all.equal(UtU, I1, tolerance=1e-12))
> 
> print(round(VtV, 12))
     [,1] [,2] [,3] [,4]
[1,]    1    0    0    0
[2,]    0    1    0    0
[3,]    0    0    1    0
[4,]    0    0    0    1
> stopifnot(all.equal(VtV, I1, tolerance=1e-12))
> 
> ## check that SVD is reproducible
> ## (this is guaranteed by a deterministic RNG built into the SVDLIBC code)
> for (i in 1:20) {
+   res <- sparsesvd(Ms)
+   if (!isTRUE(all.equal(res, res2))) stop("SVD not reproducible on iteration #", i)
+ }
> 
> proc.time()
   user  system elapsed 
   0.81    0.07    0.87