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**2256**- (**download**) (**annotate**)*Fri Sep 5 16:23:39 2008 UTC*(10 years, 11 months ago) by*mmaechler*File size: 8644 byte(s)

drop0(., tol); <Matrix,ANY> changes from JMC; diagU2N()

- 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 <dtr> %*% <dtr> - "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(<Mcl>, "[dln]Matrix") for <Mcl> 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() should ``work'': proper result or "good" error message. - make sure *all* group methods have (maybe "bail-out") setMethod for "Matrix". e.g. zapsmall(<pMatrix>) fails "badly" - rbind2(<sparse>, <dense>) does not work (e.g. <dgC>, <dge>) - <sparse> %*% <dense> {also in crossprod/tcrossprod} currently always returns <dense>, 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 - ! <symmetricMatrix> 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''. - 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! Note that 'diag(M[,-1]) <- val' is deadly slow for large sparse M, but that's because of the "outer" assignment in the equivalent M[,-1] <- `diag<-`(M[,-1], val). - examples for solve( Cholesky(.), b, system = c("A", "LDLt"....)) probably rather in man/CHMfactor-class.Rd than man/Cholesky.Rd - LDL(<CHMsimpl>) looks relatively easy; via "tCsparse_diag()" {diagonal entries of *triangular* Csparse} --> see comment in determinant(<dsC>) 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. <sparse> %*% <dense> {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(<sparse>) may differ from qr.R(<dense>) because of permutations" This is not really acceptable and currently influences rcond() as well. - 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. - implement fast diag(<triangularCsparse>) via calling new src/Csparse.c's diag_tC_ptr() - add examples (and tests!) for update(<CHMfactor>, ..) and Cholesky(......, Imult), also tests for hidden {hence no examples} ldetL2up() { R/CHMfactor.R } - chol(<nsCMatrix>) gives "temporarily disabled" but should give the *symbolic* factorization; similarly Cholesky(.) is not enabled - writeMM(obj, file=stdout()) creates file "1" since file is silently assumed to be a string, i.e. cannot be a connection. An R (instead of C) version should be pretty simple, and would work with connections automatically ["lsparse" become either "real" or "pattern", "depending if they have NAs or not]. - <diagMatrix> o <ddenseMatrix> currently works via sparse, but should return <diagMatrix> in the same cases where <diagMatrix> o <numeric> does. - look at solve.QP.compact() in \pkg{quadprog} and how to do that using our sparse matrices. Maybe this needs to be re-implemented using CHOLMOD routines. - We allow "over-allocated" (i,x)-slots for CsparseMatrix objects, as per Csparse_validate() and the tests in tests/validObj.R. This is as in CHOLMOD/CSparse, where nzmax (>= .@p[n]) corresponds to length(.@i), and makes sense e.g. for M[.,.] <- v assignments which could allocate in chunks and would not need to re-allocate anything in many cases. HOWEVER, replCmat() in R/Csparse.R is still far from making use of that. - advertize rbid2() / cbind2() and (rather?) rBind() / cBind() ------ ----- in all vignettes / talks / ... !! People erronously try rbind/cbind see that they don't work and then reinvent the wheel!

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