lsq: Least Squares Problems in Surveying
In SparseM: Sparse Linear Algebra

Description Usage Format References See Also Examples

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

data(lsq)

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.
xexactvector of the exact solutions, b, if they exist; a null vector o therwise.
guessvector of the initial guess of the solutions if they exist; a null vector otherwise.
dimdimenson of the coefficient matrix, X.
rhs.dimdimenson of the right-hand-side, y.
rhs.modestorage mode of the right-hand-side; can be full storage or same format as the coefficient matrix.

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

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

read.matrix.hb

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