BLAS daxpy functionality

This function implements the function Y := A * X + Y where X and Y may be either native double-precision valued R matrices or numeric vectors, or double-precision valued big.matrix objects, and A is a scalar.

daxpy(A = 1, X, Y)

Arguments

A	Optional numeric scalar value to scale the matrix X by, with a default value of 1.
X	Requried to be either a native R matrix or numeric vector, or a big.matrix object
Y	Optional native R matrix or numeric vector, or a big.matrix object

Value

The output value depends on the classes of input values X and Y and on the value of the global option bigalgebra.mixed_arithmetic_returns_R_matrix.

If X and Y are both big matrices, or Y is missing, options("bigalgebra.mixed_arithmetic_returns_R_matrix") is FALSE, then a big.matrix is returned. The returned big.matrix is backed by a temporary file mapping that will be deleted when the returned result is garbage collected by R (see the examples).

Otherwise, a standard R matrix is returned. The dimensional shape of the output is taken from X. If input X is dimensionless (that is, lacks a dimension attribute), then the output is a column vector.

Details

At least one of either X or Y must be a big.matrix. All values must be of type double (the only type presently supported by the bigalgebra package).

This function is rarely necessary to use directly since the bigalgebra package defines standard arithmetic operations and scalar multiplication. It is more efficient to use daxpy directly when both scaling and matrix addition are required, in which case both operations are performed in one step.

References

https://www.netlib.org/blas/daxpy.f

See also

bigmemory

Author

Michael J. Kane

Examples

require(bigmemory)
A = matrix(1, nrow=3, ncol=2)
B <- big.matrix(3, 2, type="double", init=0,
               dimnames=list(NULL, c("alpha", "beta")), shared=FALSE)
C = B + B   # C is a new big matrix
D = A + B   # D defaults to a regular R matrix, to change this, set the option:
# options(bigalgebra.mixed_arithmetic_returns_R_matrix=FALSE)
E = daxpy(A=1.0, X=B, Y=B)  # Same kind of result as C
print(C[])
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    0    0
#> [3,]    0    0
print(D)
#>      [,1] [,2]
#> [1,]    1    1
#> [2,]    1    1
#> [3,]    1    1
print(E[])
#>      [,1] [,2]
#> [1,]    0    0
#> [2,]    0    0
#> [3,]    0    0

# The C and E big.matrix file backings will be deleted when garbage collected:
# (We enable debugging to see this explicitly)
options(bigalgebra.DEBUG=TRUE)
rm(C,E)
gc()
#>           used (Mb) gc trigger (Mb) limit (Mb) max used (Mb)
#> Ncells  785189 42.0    1311267 70.1         NA  1311267 70.1
#> Vcells 1473502 11.3    8388608 64.0      65536  2216094 17.0