- Check for DimNames propagation in coercion and other operations.
- Report the problem in the Linux ldexp manual page. The second and
third calls in the Synopsis should be to ldexpf and ldexpl.
- provide methods for "dspMatrix" and "dppMatrix"!
- implement (more) methods for supporting "packed" (symmetric / triangular)
matrices; particularly something like pack() and unpack() [to/from our
classes from/to "numeric"] --- have already man/unpack.Rd but no method yet!
(have some dtr* <-> dtp*)
- combine the C functions for multiplication by special forms and
solution wrt special forms by using a 'right' argument and a
'classed' argument.
[done with dgeMatrix_matrix_mm(); not yet for other classes;
and for _crossprod()]
-----
- "Math2" , "Math", "Arith":
keep triangular and symmetric Matrices when appropriate:
particularly desirable for "Math2": round(), signif()
For triangular matrices, more specifically make sure the four rules of
"triangular matrix algebra" (Golub+Van Loan 1996, 3.1.8, p.93) are
fulfilled; now(2008-03-06) ok for Csparse; not yet for %*%
- "d" <-> "l" coercion for all "[TCR]" sparse matrices is really trivial:
"d" -> "l" : drops the 'x' slot
"l" -> "d" : construct an 'x' slot of all '1'
We currently have many of these conversions explicitly, e.g.
setAs("dsTMatrix", "lsTMatrix",
function(from) new("lsTMatrix", i = from@i, j = from@j, uplo = from@uplo,
Dim = from@Dim, Dimnames = from@Dimnames))
but I would rather want to automatically construct all these coercion
methods at once by a ``method constructor'', i.e.,
for all "dsparse*" -> "lsparse*" and vice versa.
How can one do this {in a documented way} ?
- Think of constructing setAs(...) calls automatically in order to
basically enable all ``sensible'' as(fromMatrix, toMatrix) calls,
possibly using canCoerce(.)
- setAs(, "[dln]Matrix") for in {Matrix or denseMatrix + sparseMatrix}
- When we have a packed matrix, it's a waste to go through "full" to "sparse":
==> implement
setAs("dspMatrix", "sparseMatrix")
setAs("dppMatrix", "sparseMatrix")
setAs("dtpMatrix", "sparseMatrix")
and the same for "lsp" , "ltp" and "nsp" , "ntp" !
- tcrossprod(x, y) : do provide methods for y != NULL
calling Lapack's DGEMM for "dense"
[2005-12-xx: done for dgeMatrix at least]
- BUGlet: Shouldn't lose factorization here:
h6 <- Hilbert(6); chol(h6) ; str(h6) # has factor
str(H6 <- as(h6, "dspMatrix")) # has lost factor
## and the same in a similar situation involving "dpo", "dpp"
- Factorizations: LU done; also Schur() for *sparse* Matrices.
- is.na() method for all our matrices [ ==> which(*, arr.ind=TRUE) might work ]
- use .Call(Csparse_drop, M, tol) in more places,
both with 'tol = 0.' to drop "values that happen to be 0" and for
zapsmall() methods for Csparse*
- implement .Call(Csparse_scale, ....) interfacing to cholmod_scale()
in src/CHOLMOD/Include/cholmod_matrixops.h : for another function
specifically for multiplying a cholmod_sparse object by a diagonal matrix.
Use it in %*% and [t]crossprod methods.
- chol() and determinant() should ``work'': proper result or "good" error
message.
- make sure *all* group methods have (maybe "bail-out") setMethod for "Matrix".
e.g. zapsmall() fails "badly"
- sum(): implement methods which work for *all* our matrices.
- Implement expand(.) for the Cholesky() results
"dCHMsimpl" and "dCHMsuper" -- currently have no *decent* way to get at
the matrix factors of the corresponding matrix factorization !!
- rbind2(, ) does not work (e.g. , )
- %*% {also in crossprod/tcrossprod} currently always
returns , since --> Csparse_dense_prod --> cholmod_sdmult
and that does only return dense.
When the sparse matrix is very sparse, i.e. has many rows with only zero
entries, it would make much sense to return sparse.
- sparse-symmetric + diagonal should stay sparse-symmetric
(only stays sparse): Matrix(0, 4, 4) + Diagonal(4, 1:4)
--> R/diagMatrix.R ('FIXME')
but also R/Ops.R to ensure sp-sym. + sp-sym. |-> sp-sym. etc
- Diagonal(n) %*% A --- too slow!! --> ~/R/MM/Pkg-ex/Matrix/diag-Tamas-ex.R
- ! loses symmetry, both for dense and sparse matrices.
!M where M is "sparseMatrix", currently always gives dense. This only
makes sense when M is ``really sparse''.
- msy <- as(matrix(c(2:1,1:2),2), "dsyMatrix"); str(msy)
shows that the Cholesky factorization is computed ``too quickly''.
Can be a big pain for largish matrices, when it is unneeded.
- example(Cholesky, echo=FALSE) ; cm <- chol(mtm); str(cm); str(mtm)
shows that chol() does not seems to make use of an already
present factorization and rather uses one with more '0' in x slot.
- diag(m) <- val currently automatically works via m[cbind(i,i)] <- val
This (`[<-` method) is now "smart" for diagonalMatrix, but needs also to
be for triangularMatrix, and probably also "dense*general*Matrix" since the
above currently goes via "matrix" and back instead of using the 'x' slot
directly; in particular, the triangular* "class property" is lost!
- image(M, ..): Think about an optional smart option which keeps
"0 |-> transparent" and allows colors to differentiate negative and
positive entries.
- examples for solve( Cholesky(.), b, system = c("A", "LDLt"....))
probably rather in man/CHMfactor-class.Rd than man/Cholesky.Rd
- LDL() looks relatively easy; via "tCsparse_diag()"
{diagonal entries of *triangular* Csparse}
--> see comment in determinant() in R/dsCMatrix.R, will give
faster determinant
- tr(A %*% B) {and even tr(A %*% B %*% C) ...} are also needed
frequently in some computations {conditional normal distr. ...}.
Since this can be done faster than by
sum(diag(A %*% B)) even for traditional matrices, e.g.
sum(A * t(B)) or {even faster for "full" mat}
crossprod(as.vector(A), as.vector(B))
and even more so for, e.g. %*%
{used in Soeren's 'gR' computations},
we should also provide a generic and methods.
- qr.R(qr(x)) may differ for the "same" matrix, depending on it being
sparse or dense:
"qr.R() may differ from qr.R() because of permutations"
This is not really acceptable and currently influences rcond() as well.
- chol() and qr() generic: currently have *two* arguments, and give the msg
> New generic for "chol" does not agree with implicit generic from package
> "base"; a new generic will be assigned with package "Matrix"
(and ditto for "qr")
It was mentioned by an R-core member that he thought it did not make
sense to also dispatch on 'tol' or 'pivot' ... --> maybe change that..
- eigen() should become generic, and get a method at least for diagonal,
but also for symmetric -> dsyMatrix [LAPACK dsyev() uses UPLO !],
but also simply for dgeMatrix (without going via tradition matrices).
What about Sparse? There's fill-in, but it may still be sensible, e.g.
mlist <- list(1, 2:3, diag(x=5:3), 27, cbind(1,3:6), 100:101)
ee <- eigen(tcrossprod(bdiag(lapply(mlist, as.matrix))))
Matrix( signif(ee$vectors, 3) )
- facmul() has no single method defined; it looks like a good idea though
(instead of the infamous qr.qy, qr.qty,.... functions)
- symmpart() and skewpart() for *sparse* matrices still use (x +/- t(x))/2
and could be made more efficient.
Consider going via asTuniq() or something very close to
.Arith.Csparse() in R/Ops.R
- many setAs(*, "[dl]..Matrix") are still needed, as long as e.g.
replCmat() uses as_CspClass() and drop0(.) which itself call
as_CspClass() quite a bit. --> try to replace these by
as(*, "CsparseMatrix"); forceSymmetric, etc.
- add examples (and tests!) for update(, ..) and
Cholesky(......, Imult), also tests for hidden {hence no examples}
ldetL2up() { R/CHMfactor.R }