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from scipy.optimize import minimize, LinearConstraint, rosen, rosen_der, rosen_hess
import numpy as np
import matplotlib.pyplot as plt
def test1():
fun = lambda x: x**2 + 2*x - 3
x0 = np.asarray(1)
res = minimize(fun, x0, bounds=[[0, None]], )
print(res)
def test2():
fun = lambda x: (x[0]-2)**2 + 4*(x[1]-1)**2
x0 = [0, 0]
cons = ({'type': 'ineq', 'fun': lambda x: 2 - x[0] - 2*x[1]})
res = minimize(fun, np.asarray(x0),
method='slsqp',
constraints=cons, options={'disp': True})
print(res)
def test3():
fun = lambda x: -x[0]**2*x[1]
x0 = np.asarray([0, 0])
cons = ({'type': 'eq', 'fun': lambda x: x[0]**2+x[1]**2-1})
res = minimize(fun, x0, constraints=cons, options={'disp': True})
print(res)
def example_16_4():
func = lambda x: (x[0]-1)**2 + (x[1] - 2.5)**2
x0 = np.asarray([0, 0])
# bounds = np.asarray([[0, None],
# [0, None]])
cons = ({'type': 'ineq', 'fun': lambda x: x[0] - 2*x[1] + 2},
{'type': 'ineq', 'fun': lambda x: -x[0] - 2*x[1] + 6},
{'type': 'ineq', 'fun': lambda x: -x[0] + 2*x[1] + 2},
{'type': 'ineq', 'fun': lambda x: x[0]},
{'type': 'ineq', 'fun': lambda x: x[1]})
res = minimize(func, x0,
# bounds=bounds,
constraints=cons,
jac=lambda x: np.asarray([2*(x[0]-1), 2*(x[1] - 2.5)]),
# hess=lambda x: np.asarray([[2, 0], [0, 2]]),
method='SLSQP')
print(res)
if __name__ == '__main__':
example_16_4()
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