mirror of https://github.com/pyodide/pyodide.git
51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
def test_nlopt(selenium):
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selenium.load_package("nlopt")
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assert selenium.run(
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"""
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import numpy as np
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import nlopt
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# objective function
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def f(x, grad):
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x0 = x[0]
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x1 = x[1]
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y = 67.8306620138889-13.5689721666667*x0-3.83269458333333*x1+\
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0.720841066666667*x0**2+0.3427605*x0*x1+\
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0.0640322916666664*x1**2
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grad[0] = 1.44168213333333*x0 + 0.3427605*x1 - 13.5689721666667
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grad[1] = 0.3427605*x0 + 0.128064583333333*x1 - 3.83269458333333
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return y
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# inequality constraint (constrained to be <= 0)
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def h(x, grad):
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x0 = x[0]
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x1 = x[1]
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z = -3.72589930555515+128.965158333333*x0+0.341479166666643*x1-\
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0.19642666666667*x0**2+2.78692500000002*x0*x1-\
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0.0000104166666686543*x1**2-468.897287036862
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grad[0] = -0.39285333333334*x0 + 2.78692500000002*x1 + 128.965158333333
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grad[1] = 2.78692500000002*x0 - 2.08333333373086e-5*x1 + 0.341479166666643
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return z
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opt = nlopt.opt(nlopt.LD_SLSQP, 2)
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opt.set_min_objective(f)
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opt.set_lower_bounds(np.array([2.5, 7]))
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opt.set_upper_bounds(np.array([7.5, 15]))
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opt.add_inequality_constraint(h)
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opt.set_ftol_rel(1.0e-6)
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x0 = np.array([5, 11])
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xopt = opt.optimize(x0)
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np.linalg.norm(xopt - np.array([2.746310775, 15.0])) < 1e-7
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"""
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)
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