linfun {optiSolve}

Linear Objective Function

Description

Define a linear objective function of the form

f(x) = a' x + d

.

Usage

linfun(a, d=0, id=1:length(a), name="lin.fun")

Arguments

a

Numeric vector of the coefficients.

d

Numeric value.

id

Vector defining the names of the variables to which the function applies. Each variable name corresponds to one component of x. Variable names must be consistent across constraints.

name

Name for the objective function.

Details

Define linear objective function of the form

f(x) = a' x + d

.

Value

An object of class linFun.

See Also

The main function for solving constrained programming problems is solvecop.

Examples

### Linear programming with linear and quadratic constraints ###
### Example from animal breeding                             ###
### The mean breeding value BV is maximized whereas the      ###
### mean kinship in the offspring x'Qx+d is restricted       ###

data(phenotype)
data(myQ)

A   <- t(model.matrix(~Sex-1, data=phenotype))
A[,1:5]
val <- c(0.5, 0.5)
dir <- c("==","==")

mycop <- cop(f  = linfun(a=phenotype$BV, id=phenotype$Indiv, name="BV"),
             max= TRUE,
             lb = lbcon(0,  id=phenotype$Indiv), 
             ub = ubcon(NA, id=phenotype$Indiv),
             lc = lincon(A=A, dir=dir, val=val, id=phenotype$Indiv),
             qc = quadcon(Q=myQ, d=0.001, val=0.035, name="Kinship", id=rownames(myQ)))

res <- solvecop(mycop, solver="cccp2", quiet=FALSE)

validate(mycop, res)

#            valid solver  status
#             TRUE  cccp2 optimal
#
#   Variable     Value      Bound    OK?
#   -------------------------------------
#   BV           0.7667 max        :      
#   -------------------------------------
#   lower bounds all x  >=  lb     : TRUE 
#   Sexfemale    0.5    ==  0.5    : TRUE 
#   Sexmale      0.5    ==  0.5    : TRUE 
#   Kinship      0.035  <=  0.035  : TRUE 
#   -------------------------------------