CHMfactor-class {Matrix} | R Documentation

## CHOLMOD-based Cholesky Factorizations

### Description

The virtual class `"CHMfactor"` is a class of CHOLMOD-based Cholesky factorizations of symmetric, sparse, compressed, column-oriented matrices. Such a factorization is simplicial (virtual class `"CHMsimpl"`) or supernodal (virtual class `"CHMsuper"`). Objects that inherit from these classes are either numeric factorizations (classes `"dCHMsimpl"` and `"dCHMsuper"`) or symbolic factorizations (classes `"nCHMsimpl"` and `"nCHMsuper"`).

### Usage

    isLDL(x)

    ## S4 method for signature 'CHMfactor'
    update(object, parent, mult = 0, ...)
    .updateCHMfactor(object, parent, mult)

    ## and many more methods, notably,
    ##   solve(a, b, system = c("A","LDLt","LD","DLt","L","Lt","D","P","Pt"), ...)
    ##   -----    see below

### Arguments

`x,object,a` | a `"CHMfactor"` object (almost always the result of `Cholesky()`).

`parent`

a `"dsCMatrix"` or `"dgCMatrix"` matrix object with the same nonzero pattern as the matrix that generated `object`. If `parent` is symmetric, of class `"dsCMatrix"`, then `object` should be a decomposition of a matrix with the same nonzero pattern as `parent`. If `parent` is not symmetric then `object` should be the decomposition of a matrix with the same nonzero pattern as `tcrossprod(parent)`.

Since Matrix version 1.0-8, other `"sparseMatrix"` matrices are coerced to `dsparseMatrix` and `CsparseMatrix` if needed.

`mult` | a numeric scalar (default 0). `mult` times the identity matrix is (implicitly) added to `parent` or `tcrossprod(parent)` before updating the decomposition `object`.
`...` | potentially further arguments to the methods.

### Objects from the Class

Objects can be created by calls of the form `new("dCHMsuper", ...)` but are more commonly created via `Cholesky()`, applied to `dsCMatrix` or `lsCMatrix` objects.

For an introduction, it may be helpful to look at the `expand()` method and examples below.

### Slots

of `"CHMfactor"` and all classes inheriting from it:

`perm`:

  An integer vector giving the 0-based permutation of the rows and columns chosen to reduce fill-in and for post-ordering.

`colcount`:

  Object of class `"integer"` ....

`type`:

  Object of class `"integer"` ....

Slots of the non virtual classes “[dl]CHM(super|simpl)”:

`p`:

  Object of class `"integer"` of pointers, one for each column, to the initial (zero-based) index of elements in the column. Only present in classes that contain `"CHMsimpl"`.

`i`:

  Object of class `"integer"` of length nnzero (number of non-zero elements). These are the row numbers for each non-zero element in the matrix. Only present in classes that contain `"CHMsimpl"`.

`x`:

  For the `"d*"` classes: `"numeric"` - the non-zero elements of the matrix.

### Methods

isLDL

  `(x)` returns a `logical` indicating if `x` is an LDL' decomposition or (when `FALSE`) an LL' one.

coerce

  `signature(from = "CHMfactor", to = "sparseMatrix")` (or equivalently, `to = "Matrix"` or `to = "triangularMatrix"`)

  `as(*, "sparseMatrix")` returns the lower triangular factor L from the LL' form of the Cholesky factorization. Note that (currently) the factor from the LL' form is always returned, even if the `"CHMfactor"` object represents an LDL' decomposition. Furthermore, this is the factor after any fill-reducing permutation has been applied. See the `expand` method for obtaining both the permutation matrix, P, and the lower Cholesky factor, L.

coerce

  `signature(from = "CHMfactor", to = "pMatrix")` returns the permutation matrix P, representing the fill-reducing permutation used in the decomposition.

expand

  `signature(x = "CHMfactor")` returns a list with components `P`, the matrix representing the fill-reducing permutation, and `L`, the lower triangular Cholesky factor. The original positive-definite matrix A corresponds to the product A = P'LL'P. Because of fill-in during the decomposition the product may apparently have more non-zeros than the original matrix, even after applying `drop0` to it. However, the extra "non-zeros" should be very small in magnitude.

image

  `signature(x = "CHMfactor"):` Plot the image of the lower triangular factor, L, from the decomposition. This method is equivalent to `image(as(x, "sparseMatrix"))` so the comments in the above description of the `coerce` method apply here too.

solve

  `signature(a = "CHMfactor", b = "ddenseMatrix"), system= *`: The `solve` methods for a `"CHMfactor"` object take an optional third argument `system` whose value can be one of the character strings `"A"`, `"LDLt"`, `"LD"`, `"DLt"`, `"L"`, `"Lt"`, `"D"`, `"P"` or `"Pt"`. This argument describes the system to be solved. The default, `"A"`, is to solve Ax = b for x where `A` is the sparse, positive-definite matrix that was factored to produce `a`. Analogously, `system = "L"` returns the solution x, of Lx = b. Similarly, for all system codes but `"P"` and `"Pt"` where, e.g., `x <- solve(a, b, system="P")` is equivalent to `x <- P %*% b`.

  See also `solve-methods`.

determinant

  `signature(x = "CHMfactor", logarithm = "logical")` returns the determinant (or the logarithm of the determinant, if `logarithm = TRUE`, the default) of the factor L from the LL' decomposition (even if the decomposition represented by `x` is of the LDL' form (!)). This is the square root of the determinant (half the logarithm of the determinant when `logarithm = TRUE`) of the positive-definite matrix that was decomposed.

update

  `signature(object = "CHMfactor"), parent`. The `update` method requires an additional argument `parent`, which is either a `"dsCMatrix"` object, say A, (with the same structure of nonzeros as the matrix that was decomposed to produce `object`) or a general `"dgCMatrix"`, say M, where A := M M' (`== tcrossprod(parent)`) is used for A. Further it provides an optional argument `mult`, a numeric scalar. This method updates the numeric values in `object` to the decomposition of A+mI where A is the matrix above (either the `parent` or M M') and m is the scalar `mult`. Because only the numeric values are updated this method should be faster than creating and decomposing A+mI. It is not uncommon to want, say, the determinant of A+mI for many different values of m. This method would be the preferred approach in such cases.

### See Also

`Cholesky`, also for examples; class `dgCMatrix`.

### Examples

    ## An example for the expand() method
    n <- 1000; m <- 200; nnz <- 2000
    set.seed(1)
    M1 <- spMatrix(n, m,
                   i = sample(n, nnz, replace = TRUE),
                   j = sample(m, nnz, replace = TRUE),
                   x = round(rnorm(nnz),1))
    XX <- crossprod(M1) ## = M1'M1  = M M'  where M <- t(M1)
    CX <- Cholesky(XX)
    isLDL(CX)
    str(CX) ## a "dCHMsimpl" object
    r <- expand(CX)
    L.P <- with(r, crossprod(L,P))  ## == L'P
    PLLP <- crossprod(L.P)          ## == (L'P)' L'P == P'LL'P  = XX = M M'
    b <- sample(m)
    stopifnot(all.equal(PLLP, XX),
              all(as.vector(solve(CX, b, system="P" )) == r$P %*% b),
              all(as.vector(solve(CX, b, system="Pt")) == t(r$P) %*% b) )

    u1 <- update(CX, XX,    mult=pi)
    u2 <- update(CX, t(M1), mult=pi) # with the original M, where XX = M M'
    stopifnot(all.equal(u1,u2, tol=1e-14))

       ## [ See  help(Cholesky)  for more examples ]
       ##        -------------

[Package Matrix version 1.2-17 Index]