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Diff of /pkg/TODO

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revision 1578, Mon Sep 18 21:02:50 2006 UTC revision 2005, Wed Jul 18 14:46:20 2007 UTC
# Line 56  Line 56 
56    calling Lapack's DGEMM for "dense"    calling Lapack's DGEMM for "dense"
57    [2005-12-xx: done for dgeMatrix at least]    [2005-12-xx: done for dgeMatrix at least]
58    
 - "ltTmatrix" :  
   2) new("ltTMatrix", diag = "U", Dim = c(2:2, 2:2)) shows lack of ltTM*_validate  
   
59  - BUGlet:  Shouldn't lose factorization here:  - BUGlet:  Shouldn't lose factorization here:
60    h6 <- Hilbert(6); chol(h6) ; str(h6) # has factor    h6 <- Hilbert(6); chol(h6) ; str(h6) # has factor
61    str(H6 <- as(h6, "dspMatrix"))       # has lost factor    str(H6 <- as(h6, "dspMatrix"))       # has lost factor
62    ## and the same in a similar situation involving  "dpo", "dpp"    ## and the same in a similar situation involving  "dpo", "dpp"
63    
 - "Compare" for dgC & dgT  --- want to be able to do  M[M > 10]  etc  
   
64  - Things like  M[upper.tri(M)] are not really most useful for  sparse  - Things like  M[upper.tri(M)] are not really most useful for  sparse
65    matrices.  --> provide generic functions    matrices.  --> provide generic functions
66    upperTriMatrix(), lowerTriMatrix()  both with argument  'diag = TRUE'    upperTriMatrix(), lowerTriMatrix()  both with argument  'diag = TRUE'
67    (which can be set to FALSE of course) which are used to extract a    (which can be set to FALSE of course) which are used to extract a
68    triangle from an arbitrary sparse matrix and  return a  "dtCMatrix".    triangle from an arbitrary sparse matrix and  return a  "dtCMatrix".
69    
70  - Factorizations: LU [ -> solve() ] and Schur()  for  *sparse*  Matrices.  - Factorizations: LU done; also Schur()  for  *sparse*  Matrices.
   -----------------  LU-sparse is the purpose of all of ./src/UMFPACK/  
         -->  ./inst/doc/UFsparse/*  
   Note that UMFPACK is a bit slow compared to TAUCS etc, according to  
   Gould et al.(2005)  
   And then, possibly eigen(), svd(), qr(), ... for sparse  
   {probably base some on the fast  chol() of CHOLMOD}.  
   ./man/lu.Rd notes that lu() is not yet available for sparse  
71    
72    2006-04-15 : now with 'CSparse' { src/cs.c }, have at least solve( <dtC*> )  - band(), triu(), tril() for *all* including "matrix", not just sparse matrices
73    
74  - nicer pedigree examples and tests  - is.na() method for all our matrices [ ==> which(*, arr.ind=TRUE) might work ]
75    
76  - band(), triu(), tril() for *all*, not just sparse matrices  - When we have a packed matrix, it's a waste to go through "full" to "sparse":
77      ==> implement
78            setAs("dspMatrix", "sparseMatrix")
79            setAs("dppMatrix", "sparseMatrix")
80            setAs("dtpMatrix", "sparseMatrix")
81      and the same for "lsp" , "ltp"  and  "nsp" , "ntp" !
82    
83  - is.na() method for all our matrices [ ==> which(*, arr.ind=TRUE) might work ]  - use  .Call(Csparse_drop, M, tol) in more places,
84      both with 'tol = 0.' to drop "values that happen to be 0" and for
85      zapsmall() methods for Csparse*
86    
87    - implement .Call(Csparse_scale, ....) interfacing to cholmod_scale()
88      in src/CHOLMOD/Include/cholmod_matrixops.h : for another function
89      specifically for multiplying a cholmod_sparse object by a diagonal matrix.
90      Use it in %*% and [t]crossprod methods.
91    
92    - chol() and determinant() should ``work'': proper result or "good" error
93      message.
94    
95    - Think of constructing  setAs(...) calls automatically in order to
96      basically enable all ``sensible'' as(fromMatrix, toMatrix)  calls,
97      possibly using canCoerce(.)
98    
99    - make sure *all* group methods have (maybe "bail-out") setMethod for "Matrix".
100      e.g. zapsmall(<pMatrix>) fails "badly"
101    
102    - speedup: pass class definition to non0ind() [check all calls ..]
103    
104    - sum(): implement methods which work for *all* our matrices.
105    
106    - Implement  expand(.) for the Cholesky() results
107      "dCHMsimpl" and  "dCHMsuper"  -- currently have no *decent* way to get at
108      the matrix factors of the corresponding matrix factorization !!
109    
110    - rbind(<sparse>, <dense>) does not work  (e.g. <dgC>, <dge>)
111    
112    - setAs(<Mcl>,  "[dln]Matrix" )  for <Mcl> in {Matrix or denseMatrix + sparseMatrix}
113    
114    - Tell users about the possibility to disable the "S4-generic but somewhat slow"
115      cbind/rbind, e.g. via
116    
117      setHook(packageEvent("Matrix", "onLoad"),
118              function(...) methods:::bind_activation(FALSE))
119    
120    - ensure that  M[0], M[FALSE], M[1:2]  works as for traditional Matrices
121    
122    - make sure  M[FALSE, FALSE]  works for all Matrices
123      {e.g. fails for M <- Diagonal(4)}
124    
125    - <sparse> %*% <dense>  {also in crossprod/tcrossprod}  currently always
126      returns <dense>, since --> Csparse_dense_prod --> cholmod_sdmult
127      and that does only return dense.
128      When the sparse matrix is very sparse, i.e. has many rows with only zero
129      entries, it would make much sense to return sparse.
130    
131    - sparse-symmetric + diagonal should stay sparse-symmetric
132      (only stays sparse): Matrix(0, 4, 4) + Diagonal(4, 1:4)
133      --> R/diagMatrix.R ('FIXME')
134      but also R/Ops.R  to ensure  sp-sym. + sp-sym. |-> sp-sym.  etc
135    
136    - For a square sparse matrix 'b' {typically dgCMatrix or dgTMatrix},
137      we'd want a function  "Mat_plus_t_Mat" <- function(b) {....}
138      which computes the symmetric sparse matrix   b + t(b)
139      in way that never works with size-doubled vectors from  b@i etc..
140    
141    - ! <symmetricMatrix>  loses symmetry, both for dense and sparse matrices.
142      !M  where M is "sparseMatrix", currently always gives dense. This only
143      makes sense when M is ``really sparse''.
144    
145    - log1p(<sparseMatrix>) "should" give <dsparseMatrix>
146      Pretty surely, this would happen automagically, if "log1p" became part of
147      "Math" group generic ---> which is the case from R 2.6.0 on
148    
149    - msy <- as(matrix(c(2:1,1:2),2), "dsyMatrix"); str(msy)
150    
151      shows that the Cholesky factorization is computed ``too quickly''.
152      Can be a big pain for largish matrices, when it is unneeded.
153    
154    - diag(m) <- val    currently automatically works via  m[cbind(i,i)] <- val
155      However,
156      we need methods for 'diag<-' at least for diagonalMatrix,
157      triangularMatrix, and probably also "dense*general*Matrix" since the
158      above currently goes via "matrix" and back instead of using the 'x' slot
159      directly.

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