Least Squares Problems in Surveying

Description

One of the four matrices from the least-squares solution of problems in surveying that were used by Michael Saunders and Chris Paige in the testing of LSQR

Usage

data(lsq)

Format

A list of class matrix.csc.hb or matrix.ssc.hb depending on how the coefficient matrix is stored with the following components:

ra
ra component of the csc or ssc format of the coefficient matrix, X.
ja
ja component of the csc or ssc format of the coefficient matrix, X.
ia
ia component of the csc or ssc format of the coefficient matrix, X.
rhs.ra
ra component of the right-hand-side, y, if stored in csc or ssc format; right-hand-side stored in dense vector or matrix otherwise.
rhs.ja
ja component of the right-hand-side, y, if stored in csc or ssc format; a null vector otherwise.
rhs.ia
ia component of the right-hand-side, y, if stored in csc or ssc format; a null vector otherwise.
xexact
vector of the exact solutions, b, if they exist; a null vector o therwise.
guess
vector of the initial guess of the solutions if they exist; a null vector otherwise.
dim
dimenson of the coefficient matrix, X.
rhs.dim
dimenson of the right-hand-side, y.
rhs.mode
storage mode of the right-hand-side; can be full storage or same format as the coefficient matrix.

References

Koenker, R and Ng, P. (2002). SparseM: A Sparse Matrix Package for R,
http://www.econ.uiuc.edu/~roger/research

Matrix Market, http://math.nist.gov/MatrixMarket/data/Harwell-Boeing/lsq/lsq.html

See Also

read.matrix.hb

Examples

data(lsq)
class(lsq) # -> [1] "matrix.csc.hb"
model.matrix(lsq)->X
class(X) # -> "matrix.csr"
dim(X) # -> [1] 1850  712
y <- model.response(lsq) # extract the rhs
length(y) # [1] 1850