limSolve (version 1.5.7.1)

ldp: 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, 
    lower = NULL, upper = NULL)

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.

numiter

the number of iterations.

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 warnings and messages.

upper, lower

vector containing upper and lower bounds on the unknowns. If one value, it is assumed to apply to all unknowns. If a vector, it should have a length equal to the number of unknowns; this vector can contain NA for unbounded variables. The upper and lower bounds are added to the inequality conditions G*x>=H.

Author

Karline Soetaert <[e-mail]>

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(nrow = 2, ncol = 2, data = c(3, 2, 2, 4))
H <- c(3, 2)

ldp(G, H)

# imposing bounds on the first unknown
ldp(G, H, lower = c(1, NA))