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import numpy as np from scipy.optimize import minimize, Bounds def test_gh10880(): # checks that verbose reporting works with trust-constr for # bound-contrained problems bnds = Bounds(1, 2) opts = {'maxiter': 1000, 'verbose': 2} minimize(lambda x: x**2, x0=2., method='trust-constr', bounds=bnds, options=opts) opts = {'maxiter': 1000, 'verbose': 3} minimize(lambda x: x**2, x0=2., method='trust-constr', bounds=bnds, options=opts) def test_gh12922(): # checks that verbose reporting works with trust-constr for # general constraints def objective(x): return np.array([(np.sum((x+1)**4))]) cons = {'type': 'ineq', 'fun': lambda x: -x[0]**2} n = 25 x0 = np.linspace(-5, 5, n) opts = {'maxiter': 1000, 'verbose': 2} minimize(objective, x0=x0, method='trust-constr', constraints=cons, options=opts) opts = {'maxiter': 1000, 'verbose': 3} minimize(objective, x0=x0, method='trust-constr', constraints=cons, options=opts)
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