ldp {limSolve}R Documentation

Least Distance Programming

Description

Solves the following inverse problem:

min(sum {x_i}^2)

subject to

Gx>=h


uses least distance programming subroutine ldp (FORTRAN) from Linpack

Usage

ldp(G, H, tol=sqrt(.Machine$double.eps), verbose=TRUE)

Arguments

G numeric matrix containing the coefficients of the inequality constraints Gx>=H; if the columns of G have a names attribute, they will be used to label the output
H numeric vector containing the right-hand side of the inequality constraints
tol tolerance (for inequality constraints)
verbose logical to print ldp error messages

Value

a list containing:

X vector containing the solution of the least distance problem.
residualNorm scalar, the sum of absolute values of residuals of violated inequalities; should be zero or very small if the problem is feasible
solutionNorm scalar, the value of the quadratic function at the solution, i.e. the value of sum {w_i*x_i}^2
IsError logical, TRUE if an error occurred
type the string "ldp", such that how the solution was obtained can be traced

Author(s)

Karline Soetaert <k.soetaert@nioo.knaw.nl>

References

Lawson C.L.and Hanson R.J. 1974. Solving Least Squares Problems, Prentice-Hall
Lawson C.L.and Hanson R.J. 1995. Solving Least Squares Problems. SIAM classics in applied mathematics, Philadelphia. (reprint of book)

See Also

ldei, which includes equalities

Examples

# parsimonious (simplest) solution
G <- matrix(nr=2,nc=2,data=c(3,2,2,4))
H <- c(3,2)

ldp(G,H)

[Package limSolve version 1.3 Index]