Modelling

HiGHS has a rudimentry modelling language that allows models to be built and run using highspy.

Below is an example of building a mathematical LP. The functions used are documented in detail below

# model and solve the LP
#
# maximize 10 x1 + 25x2
# s. t.       x1 +  2x2 <=  80
#             x1 +  4x2 <= 120
#             x1 >= 0; x2 >= 0
import highspy

h = highspy.Highs()

x1 = h.addVariable()
x2 = h.addVariable()

h.addConstr(x1 + 2*x2 <=  80)
h.addConstr(x1 + 4*x2 <= 120)

h.maximize(10*x1 + 25*x2)

print("x1 = ", h.val(x1))
print("x2 = ", h.val(x2))

addVariable

Adds a variable to the model. By default it is continuous, non-negative, with zero objective coefficient, and has no name associated with it.

addVariable(lb = 0, ub = kHighsInf, obj = 0, type=HighsVarType.kContinuous, name = None)

Adds a constraint to the model. It must be defined in terms of a linear function, with * used when there are non-unit coefficients. By default it has a lower bound of -infinity, an upper bound of +infinity, and no name associated with it.

addConstr(cons, name = None)

maximize

Calls HiGHS to maximize the objective. By default it uses the objective coefficients defined when the variables were added to the model. However, a linear function can be passed as an argument.

maximize(obj=None)

minimize

Calls HiGHS to minimize the objective. By default it uses the objective coefficients defined when the variables were added to the model. However, a linear function can be passed as an argument.

minimize(obj=None)

val

Extracts the current value of a particular variable

val(var)

vals

Extracts the current values of a particular set of variables

vals(vars)