Dir_Elimination: Direct Elimination for LSE problem.

In LSE: Constrained Least Squares and Generalized QR Factorization

Description

Direct Elimination allows to give an analytic solution for equality constrained least squares problem (LSE). Requires MASS and pracma library.

Usage

Dir_Elimination(A,C,b,d)

Arguments

A	Design matrix, m rows and n columns.
C	Constraint matrix, p rows and n columns.
b	Response vector for A, Ax=b, m rows and 1 column.
d	Response vector for C, Cx=d, p rows and 1 column.

Details

Direct Elimination method gives a numerical vector as the solution of a least squares problem (Ax=b), when impose some restrictions (additional information, extramuestral information or a priori information) that lead to another linear equality system (Cx=d). See significance constraint (x=0) or inclusion restriction (x+y=1), etc.

Value

Numerical vector for a LSE problem.

Author(s)

Sergio Andrés Cabrera Miranda Statician [e-mail]

References

Lawson, C. L., & Hanson, R. J. (1974). Linear least squares with linear inequality constraints. Solving least squares problems, 158-173.

Van Benthem, M. H., Keenan, M. R., & Haaland, D. M. (2002). Application of equality constraints on variables during alternating least squares procedures. Journal of Chemometrics: A Journal of the Chemometrics Society, 16(12), 613-622.

Examples

A = matrix(runif(50,-1,1),10,5)
C = matrix(runif(20,-1,1),4,5)
b = matrix(runif(10,-1,1),10,1)
d = matrix(runif(4,-1,1),4,1)

Dir_Elimination(A,C,b,d)

LSE documentation built on Feb. 2, 2022, 5:07 p.m.