# rankMatrix: Rank of a Matrix In Matrix: Sparse and Dense Matrix Classes and Methods

 rankMatrix R Documentation

## Rank of a Matrix

### Description

Compute ‘the’ matrix rank, a well-defined functional in theory(*), somewhat ambiguous in practice. We provide several methods, the default corresponding to Matlab's definition.

(*) The rank of a n x m matrix A, rk(A), is the maximal number of linearly independent columns (or rows); hence rk(A) <= min(n,m).

### Usage

```rankMatrix(x, tol = NULL,
method = c("tolNorm2", "qr.R", "qrLINPACK", "qr",
sval = svd(x, 0, 0)\$d, warn.t = TRUE, warn.qr = TRUE)

qr2rankMatrix(qr, tol = NULL, isBqr = is.qr(qr), do.warn = TRUE)
```

### Arguments

 `x` numeric matrix, of dimension n x m, say. `tol` nonnegative number specifying a (relative, “scalefree”) tolerance for testing of “practically zero” with specific meaning depending on `method`; by default, `max(dim(x)) * .Machine\$double.eps` is according to Matlab's default (for its only method which is our `method="tolNorm2"`). `method` a character string specifying the computational method for the rank, can be abbreviated: `"tolNorm2"`:the number of singular values `>= tol * max(sval)`; `"qrLINPACK"`:for a dense matrix, this is the rank of `qr(x, tol, LAPACK=FALSE)` (which is `qr(...)\$rank`); This ("qr*", dense) version used to be the recommended way to compute a matrix rank for a while in the past. For sparse `x`, this is equivalent to `"qr.R"`. `"qr.R"`:this is the rank of triangular matrix R, where `qr()` uses LAPACK or a "sparseQR" method (see `qr-methods`) to compute the decomposition QR. The rank of R is then defined as the number of “non-zero” diagonal entries d_i of R, and “non-zero”s fulfill |d_i| >= tol * max(|d_i|). `"qr"`:is for back compatibility; for dense `x`, it corresponds to `"qrLINPACK"`, whereas for sparse `x`, it uses `"qr.R"`. For all the "qr*" methods, singular values `sval` are not used, which may be crucially important for a large sparse matrix `x`, as in that case, when `sval` is not specified, the default, computing `svd()` currently coerces `x` to a dense matrix. `"useGrad"`:considering the “gradient” of the (decreasing) singular values, the index of the smallest gap. `"maybeGrad"`:choosing method `"useGrad"` only when that seems reasonable; otherwise using `"tolNorm2"`. `sval` numeric vector of non-increasing singular values of `x`; typically unspecified and computed from `x` when needed, i.e., unless `method = "qr"`. `warn.t` logical indicating if `rankMatrix()` should warn when it needs `t(x)` instead of `x`. Currently, for `method = "qr"` only, gives a warning by default because the caller often could have passed `t(x)` directly, more efficiently. `warn.qr` in the QR cases (i.e., if `method` starts with `"qr"`), `rankMatrix()` calls `qr2rankMarix(.., do.warn = warn.qr)`, see below.
 `qr` an R object resulting from `qr(x,..)`, i.e., typically inheriting from `class` `"qr"` or `"sparseQR"`. `isBqr` `logical` indicating if `qr` is resulting from base `qr()`. (Otherwise, it is typically from Matrix package sparse `qr`.) `do.warn` logical; if true, warn about non-finite (or in the `sparseQR` case negative) diagonal entries in the R matrix of the QR decomposition. Do not change lightly!

### Details

`qr2rankMatrix()` is typically called from `rankMatrix()` for the `"qr"`* `method`s, but can be used directly - much more efficiently in case the `qr`-decomposition is available anyway.

### Value

If `x` is a matrix of all `0` (or of zero dimension), the rank is zero; otherwise, typically a positive integer in `1:min(dim(x))` with attributes detailing the method used.

There are rare cases where the sparse QR decomposition “fails” in so far as the diagonal entries of R, the d_i (see above), end with non-finite, typically `NaN` entries. Then, a warning is signalled (unless `warn.qr` / `do.warn` is not true) and `NA` (specifically, `NA_integer_`) is returned.

### Note

For large sparse matrices `x`, unless you can specify `sval` yourself, currently `method = "qr"` may be the only feasible one, as the others need `sval` and call `svd()` which currently coerces `x` to a `denseMatrix` which may be very slow or impossible, depending on the matrix dimensions.

Note that in the case of sparse `x`, `method = "qr"`, all non-strictly zero diagonal entries d_i where counted, up to including Matrix version 1.1-0, i.e., that method implicitly used `tol = 0`, see also the `set.seed(42)` example below.

### Author(s)

Martin Maechler; for the "*Grad" methods building on suggestions by Ravi Varadhan.

`qr`, `svd`.

### Examples

```rankMatrix(cbind(1, 0, 1:3)) # 2

(meths <- eval(formals(rankMatrix)\$method))

## a "border" case:
H12 <- Hilbert(12)
rankMatrix(H12, tol = 1e-20) # 12;  but  11  with default method & tol.
sapply(meths, function(.m.) rankMatrix(H12, method = .m.))
##       11     11         12   12       11        11
## The meaning of 'tol' for method="qrLINPACK" and *dense* x is not entirely "scale free"
rMQL <- function(ex, M) rankMatrix(M, method="qrLINPACK",tol = 10^-ex)
rMQR <- function(ex, M) rankMatrix(M, method="qr.R",     tol = 10^-ex)
sapply(5:15, rMQL, M = H12) # result is platform dependent
##  7  7  8 10 10 11 11 11 12 12 12  {x86_64}
sapply(5:15, rMQL, M = 1000 * H12) # not identical unfortunately
##  7  7  8 10 11 11 12 12 12 12 12
sapply(5:15, rMQR, M = H12)
##  5  6  7  8  8  9  9 10 10 11 11
sapply(5:15, rMQR, M = 1000 * H12) # the *same*

## "sparse" case:
M15 <- kronecker(diag(x=c(100,1,10)), Hilbert(5))
sapply(meths, function(.m.) rankMatrix(M15, method = .m.))
#--> all 15, but 'useGrad' has 14.
sapply(meths, function(.m.) rankMatrix(M15, method = .m., tol = 1e-7)) # all 14

## "large" sparse
n <- 250000; p <- 33; nnz <- 10000
L <- sparseMatrix(i = sample.int(n, nnz, replace=TRUE),
j = sample.int(p, nnz, replace=TRUE), x = rnorm(nnz))
(st1 <- system.time(r1 <- rankMatrix(L)))                # warning+ ~1.5 sec (2013)
(st2 <- system.time(r2 <- rankMatrix(L, method = "qr"))) # considerably faster!
r1[] == print(r2[]) ## -->  ( 33  TRUE )

## another sparse-"qr" one, which ``failed'' till 2013-11-23:
set.seed(42)
f1 <- factor(sample(50, 1000, replace=TRUE))
f2 <- factor(sample(50, 1000, replace=TRUE))
f3 <- factor(sample(50, 1000, replace=TRUE))
D <- t(do.call(rbind, lapply(list(f1,f2,f3), as, 'sparseMatrix')))
dim(D); nnzero(D) ## 1000 x 150 // 3000 non-zeros (= 2%)
stopifnot(rankMatrix(D,           method='qr') == 148,
rankMatrix(crossprod(D),method='qr') == 148)

## zero matrix has rank 0 :
stopifnot(sapply(meths, function(.m.)
rankMatrix(matrix(0, 2, 2), method = .m.)) == 0)
```

Matrix documentation built on Nov. 11, 2022, 9:06 a.m.