dot_functions: In-place computations

In dspline: Tools for Computations with Discrete Splines

In-place computations

Description

Each "dot" function accepts arguments as in its "non-dot" counterpart, but peforms computations in place, overwriting the first input argument (which must be a vector of the appropriate length) with the desired output.

Usage

.divided_diff(f, z)

.b_mat_mult(v, k, xd, tf_weighting, transpose, inverse)

.h_mat_mult(v, k, xd, di_weighting, transpose, inverse)

Arguments

f	Function, or vector of function evaluations at the centers.
z	Centers for the divided difference calculation.
v	Vector to be multiplied by B, the extended discrete derivative matrix.
k	Order for the extended discrete derivative matrix. Must be >= 0.
xd	Design points. Must be sorted in increasing order, and have length at least k+1.
tf_weighting	Should "trend filtering weighting" be used? This is a weighting of the discrete derivatives that is implicit in trend filtering; see details for more information. The default is FALSE.
transpose	Multiply by the transpose of B? The default is FALSE.
inverse	Multiply by the inverse of B? The default is FALSE.
di_weighting	Should "discrete integration weighting" be used? Multiplication by such a weighted H gives discrete integrals at the design points; see details for more information. The default is FALSE.

Details

These functions should not be used unless you are intentionally doing so for memory considerations and are nonetheless being very careful.

An important warning: each "dot" function only works as expected if its first argument is passed in as a vector of numeric type. If the first argument is passed in as an integer vector, then since the output must (in general) be a numeric vector, it cannot be computed in place (Rcpp performs an implicit cast and copy when it converts this to NumericVector type for use in C++).

Also, each "dot" function does not perform any error checking on its input arguments. Use with care. More details on the computations performed by individual functions are provided below.

Value

None. These functions overwrite their input.

.divided_diff()

Overwrites f with all lower-order divided differences: each element f[i] becomes the divided difference with respect to centers z[1:i]. See also divided_diff().

.b_mat_mult()

Overwrites v with B %*% v, where B is the extended discrete derivative matrix as returned by b_mat(). See also b_mat_mult().

.h_mat_mult()

Overwrites v with H %*% v, where H is the falling factorial basis matrix as returned by h_mat(). See also h_mat_mult().

Examples

v = as.numeric(1:10) # Note: must be of numeric type
b_mat_mult(v, 1, 1:10) 
v
.b_mat_mult(v, 1, 1:10, FALSE, FALSE, FALSE)
v

dspline documentation built on June 8, 2025, 9:40 p.m.