ipsolver_simple_example.py

# Example
from __future__ import division, print_function, absolute_import
import numpy as np
from ipsolver import minimize_constrained, NonlinearConstraint, BoxConstraint

# Define objective function and derivatives
fun = lambda x: 1/2*(x[0] - 2)**2 + 1/2*(x[1] - 1/2)**2
grad = lambda x: np.array([x[0] - 2, x[1] - 1/2])
hess =  lambda x: np.eye(2)
# Define nonlinear constraint
c = lambda x: np.array([1/(x[0] + 1) - x[1] - 1/4,])
c_jac = lambda x: np.array([[-1/(x[0] + 1)**2, -1]])
c_hess = lambda x, v: 2*v[0]*np.array([[1/(x[0] + 1)**3, 0], [0, 0]])
nonlinear = NonlinearConstraint(c, ('greater',), c_jac, c_hess)
# Define box constraint
box = BoxConstraint(("greater",))

# Define initial point
x0 = np.array([0, 0])
# Apply solver
result = minimize_constrained(fun, x0, grad, hess, (nonlinear, box))

# Print results
print('x* = ' + str(result.x))
print('optimality = ' + str(result.optimality))
print('c violation = ' + str(result.constr_violation))
print('niter = ' + str(result.niter))
print('f evals = ' + str(result.nfev))
print('CG iters = ' + str(result.cg_niter))
print('total time = ' + str(result.execution_time))