39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import numpy as np
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from scipy.optimize import fmin_bfgs, fmin_slsqp
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def cost(x):
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'''Cost f(x,y) = x^2 + 2y^2 - 2xy - 2x '''
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x0 = x[0]
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x1 = x[1]
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return -1*(2*x0*x1 + 2*x0 - x0**2 - 2*x1**2)
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def constraint_eq(x):
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''' Constraint x^3 = y '''
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return np.array([ x[0]**3-x[1] ])
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def constraint_ineq(x):
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'''Constraint x>=2, y>=2'''
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return np.array([ x[0]-2,x[1]-2 ])
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class CallbackLogger:
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def __init__(self):
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self.nfeval = 1
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def __call__(self,x):
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print('===CBK=== {0:4d} {1: 3.6f} {2: 3.6f} {3: 3.6f}'.format(self.nfeval, x[0], x[1], cost(x)))
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self.nfeval += 1
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x0 = np.array([0.0,0.0])
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# Optimize cost without any constraints in BFGS, with traces.
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xopt_bfgs = fmin_bfgs(cost, x0, callback=CallbackLogger())
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print('\n *** Xopt in BFGS = ',xopt_bfgs,'\n\n\n\n')
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# Optimize cost without any constraints in CLSQ
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xopt_lsq = fmin_slsqp(cost,[-1.0,1.0], iprint=2, full_output=1)
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print('\n *** Xopt in LSQ = ',xopt_lsq,'\n\n\n\n')
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# Optimize cost with equality and inequality constraints in CLSQ
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xopt_clsq = fmin_slsqp(cost,[-1.0,1.0],
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f_eqcons=constraint_eq, f_ieqcons=constraint_ineq,
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iprint=2, full_output=1)
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print('\n *** Xopt in c-lsq = ',xopt_clsq,'\n\n\n\n')
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