solvemethods  R Documentation 
solve
Methods for generic function solve
for solving
linear systems of equations,
i.e., for X
in A X = B
,
where A
is a square matrix and X
and B
are matrices
with dimensions consistent with A
.
solve(a, b, ...)
## S4 method for signature 'dgeMatrix,ANY'
solve(a, b, tol = .Machine$double.eps, ...)
## S4 method for signature 'dgCMatrix,missing'
solve(a, b, sparse = TRUE, ...)
## S4 method for signature 'dgCMatrix,matrix'
solve(a, b, sparse = FALSE, ...)
## S4 method for signature 'dgCMatrix,denseMatrix'
solve(a, b, sparse = FALSE, ...)
## S4 method for signature 'dgCMatrix,sparseMatrix'
solve(a, b, sparse = TRUE, ...)
## S4 method for signature 'denseLU,dgeMatrix'
solve(a, b, ...)
## S4 method for signature 'BunchKaufman,dgeMatrix'
solve(a, b, ...)
## S4 method for signature 'Cholesky,dgeMatrix'
solve(a, b, ...)
## S4 method for signature 'sparseLU,dgCMatrix'
solve(a, b, tol = .Machine$double.eps, ...)
## S4 method for signature 'sparseQR,dgCMatrix'
solve(a, b, ...)
## S4 method for signature 'CHMfactor,dgCMatrix'
solve(a, b, system = c("A", "LDLt", "LD", "DLt", "L", "Lt", "D", "P", "Pt"), ...)
a 
a finite square matrix or

b 
a vector, 
tol 
a nonnegative number. For 
sparse 
a logical indicating if the result should be formally
sparse, i.e., if the result should inherit from virtual class

system 
a string specifying a linear system to be solved.
Only methods for 
... 
further arguments passed to or from methods. 
Methods for general and symmetric matrices a
compute a
triangular factorization (LU, BunchKaufman, or Cholesky)
and call the method for the corresponding factorization class.
The factorization is sparse if a
is. Methods for sparse,
symmetric matrices a
attempt a Cholesky factorization
and perform an LU factorization only if that fails (typically
because a
is not positive definite).
Triangular, diagonal, and permutation matrices do not require
factorization (they are already “factors”), hence methods
for those are implemented directly. For triangular a
,
solutions are obtained by forward or backward substitution;
for diagonal a
, they are obtained by scaling the rows
of b
; and for permutations a
, they are obtained
by permuting the rows of b
.
Methods for dense a
are built on 14 LAPACK routines:
class d..Matrix
, where ..=(getrtpsysppopp)
,
uses routines d..tri
and d..trs
for missing
and nonmissing b
, respectively. A corollary is that
these methods always give a dense result.
Methods for sparse a
are built on CSparse routines
cs_lsolve
, cs_usolve
, and cs_spsolve
and
CHOLMOD routines cholmod_solve
and cholmod_spsolve
.
By default, these methods give a vector result if b
is a vector, a sparse matrix result if b
is missing
or a sparse matrix, and a dense matrix result if b
is a dense matrix. One can override this behaviour by setting
the sparse
argument, where available, but that should
be done with care. Note that a sparse result may be sparse only
in the formal sense and not at all in the mathematical sense,
depending on the nonzero patterns of a
and b
.
Furthermore, whereas dense results are fully preallocated,
sparse results must be “grown” in a loop over the columns
of b
.
Methods for a
of class sparseQR
are simple wrappers around qr.coef
, giving the
least squares solution in overdetermined cases.
Methods for a
inheriting from CHMfactor
can solve systems other than the default one A X = B
.
The correspondence between its system
argument the system
actually solved is outlined in the table below.
See CHMfactorclass
for a definition of notation.
system  isLDL(a)=TRUE  isLDL(a)=FALSE 
"A"  A X = B  A X = B 
"LDLt"  L_{1} D L_{1}' X = B  L L' X = B 
"LD"  L_{1} D X = B  L X = B 
"DLt"  D L_{1}' X = B  L' X = B 
"L"  L_{1} X = B  L X = B 
"Lt"  L_{1}' X = B  L' X = B 
"D"  D X = B  X = B 
"P"  X = P_{1} B  X = P_{1} B 
"Pt"  X = P_{1}' B  X = P_{1}' B

Virtual class MatrixFactorization
and its
subclasses.
Generic functions Cholesky
, BunchKaufman
,
Schur
, lu
, and qr
for
computing factorizations.
Generic function solve
from base.
Function qr.coef
from base for computing
least squares solutions of overdetermined linear systems.
## A close to symmetric example with "quite sparse" inverse:
n1 < 7; n2 < 3
dd < data.frame(a = gl(n1,n2), b = gl(n2,1,n1*n2))# balanced 2way
X < sparse.model.matrix(~ 1+ a + b, dd)# no intercept > even sparser
XXt < tcrossprod(X)
diag(XXt) < rep(c(0,0,1,0), length.out = nrow(XXt))
n < nrow(ZZ < kronecker(XXt, Diagonal(x=c(4,1))))
image(a < 2*Diagonal(n) + ZZ %*% Diagonal(x=c(10, rep(1, n1))))
isSymmetric(a) # FALSE
image(drop0(skewpart(a)))
image(ia0 < solve(a, tol = 0)) # checker board, dense [but really, a is singular!]
try(solve(a, sparse=TRUE))##> error [ TODO: assertError ]
ia. < solve(a, sparse=TRUE, tol = 1e19)##> *no* error
if(R.version$arch == "x86_64")
## Fails on 32bit [Fedora 19, R 3.0.2] from Matrix 1.10 on [FIXME ??] only
stopifnot(all.equal(as.matrix(ia.), as.matrix(ia0)))
a < a + Diagonal(n)
iad < solve(a)
ias < solve(a, sparse=FALSE)
stopifnot(all.equal(as(iad,"denseMatrix"), ias, tolerance=1e14))
I. < iad %*% a ; image(I.)
I0 < drop0(zapsmall(I.)); image(I0)
.I < a %*% iad
.I0 < drop0(zapsmall(.I))
stopifnot( all.equal(as(I0, "diagonalMatrix"), Diagonal(n)),
all.equal(as(.I0,"diagonalMatrix"), Diagonal(n)) )
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