highs_model {highs}

Create a Highs Model

Description

Solve linear and quadratic mixed integer optimization problems.

Usage

highs_model(
  Q = NULL,
  L,
  lower,
  upper,
  A,
  lhs,
  rhs,
  types,
  maximum = FALSE,
  offset = 0
)

Arguments

Q

a numeric symmetric matrix giving the quadratic part of the objective.

L

a numeric vector giving the linear part of the objective function.

lower

a numeric vector giving the lower bounds of the variables.

upper

a numeric vector giving the upper bounds of the variables.

A

a numeric matrix giving the linear part of the constraints. Rows are constraints, and columns are decision variables.

lhs

a numeric vector giving the left hand-side of the linear constraints.

rhs

a numeric vector giving the right hand-side of the linear constraints.

types

a integer vector or character vector giving the variable types. 'C' or '1' for continuous, 'I' or '2' for integer, 'SC' or '3' for semi continuous, 'SI' or '4' for semi integer and 'II' or '5' for implicit integer.

maximum

a logical if TRUE the solver searches for a maximum, if FALSE the solver searches for a minimum.

offset

a numeric value giving the offset (default is 0).

Value

A an object of class highs_model.

Examples

library("highs")
# Minimize:
#  x_0 +  x_1 + 3
# Subject to:
#               x_1 <=  7
#  5 <=  x_0 + 2x_1 <= 15
#  6 <= 3x_0 + 2x_1
#  0 <= x_0 <= 4
#  1 <= x_1
A <- rbind(c(0, 1), c(1, 2), c(3, 2))
m <- highs_model(L = c(1.0, 1), lower = c(0, 1), upper = c(4, Inf),
                 A = A, lhs = c(-Inf, 5, 6), rhs = c(7, 15, Inf),
                 offset = 3)
m

# Minimize:
#  -x_2 - 3x_3 + (1/2) * (2 x_1^2 - 2 x_1x_3 + 0.2 x_2^2 + 2 x_3^2)
# Subject to:
#  x_1 + x_3 <= 2
#  0 <= x
L <- c(0, -1, -3)
Q <- rbind(c(2, 0.0, -1), c(0, 0.2, 0), c(-1, 0.0, 2))
A <- cbind(1, 0, 1)
m <- highs_model(Q = Q, L = L, lower = 0, A = A, rhs = 2)
m