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from scipy.optimize import minimize, LinearConstraint
import numpy as np
def test1():
fun = lambda x: x**2 + 2*x - 3
x0 = 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)
if __name__ == '__main__':
test3()
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