gc_funcnonlinear.py
#!/usr/bin/env python3.11
# Copyright 2024, Gurobi Optimization, LLC
# This example considers the following nonconvex nonlinear problem
#
# minimize sin(x) + cos(2*x) + 1
# subject to 0.25*exp(x) - x <= 0
# -1 <= x <= 4
#
# We show you two approaches to solve it as a nonlinear model:
#
# 1) Set the paramter FuncNonlinear = 1 to handle all general function
# constraints as true nonlinear functions.
#
# 2) Set the attribute FuncNonlinear = 1 for each general function
# constraint to handle these as true nonlinear functions.
#
import gurobipy as gp
from gurobipy import GRB
def printsol(m, x):
print(f"x = {x.X}")
print(f"Obj = {m.ObjVal}")
try:
# Create a new model
m = gp.Model()
# Create variables
x = m.addVar(lb=-1, ub=4, name="x")
twox = m.addVar(lb=-2, ub=8, name="2x")
sinx = m.addVar(lb=-1, ub=1, name="sinx")
cos2x = m.addVar(lb=-1, ub=1, name="cos2x")
expx = m.addVar(name="expx")
# Set objective
m.setObjective(sinx + cos2x + 1, GRB.MINIMIZE)
# Add linear constraints
lc1 = m.addConstr(0.25 * expx - x <= 0)
lc2 = m.addConstr(2.0 * x - twox == 0)
# Add general function constraints
# sinx = sin(x)
gc1 = m.addGenConstrSin(x, sinx, "gc1")
# cos2x = cos(twox)
gc2 = m.addGenConstrCos(twox, cos2x, "gc2")
# expx = exp(x)
gc3 = m.addGenConstrExp(x, expx, "gc3")
# Approach 1) Set FuncNonlinear parameter
m.params.FuncNonlinear = 1
# Optimize the model
m.optimize()
printsol(m, x)
# Restore unsolved state and set parameter FuncNonlinear to
# its default value
m.reset()
m.resetParams()
# Approach 2) Set FuncNonlinear attribute for every
# general function constraint
gc1.funcnonlinear = 1
gc2.funcnonlinear = 1
gc3.funcnonlinear = 1
m.optimize()
printsol(m, x)
except gp.GurobiError as e:
print(f"Error code {e.errno}: {e}")
except AttributeError:
print("Encountered an attribute error")
