以下是我的代码。我得到标题中提到的 ValueError(并在最后附加),我无法想象为什么。我的函数是 R^2 -> R,我密切关注(格式,而不是实际值)中的步骤(http://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#constrained-最小化多变量标量函数最小化)。这就是为什么我不理解维度问题,一切都与那里非常相似。
我的代码:
def func(x, theta, sign=1.0):
return sign*(math.log(x[0]) + theta*math.log(1-x[1]))
def func_deriv (x, theta, sign=1.0):
dfdc = (1/x[0])
dfdn = theta*1/(1-x[1])*(-1)
return sign*array([ dfdc, dfdn])
cons = (
{'type':'eq',
'fun' : lambda x: array([
exp(e)*k**alpha*x[1]**(1-alpha) - (kPrime - k*(1-delta))
- phi/2*(kPrime/k - delta)**2 - x[0] ]),
'jac' : lambda x: array([
-1, (1-alpha)*exp(e)*k**alpha*x[1]**(-alpha)
])
},
{'type':'ineq',
'fun' : lambda x: array([x[0]]),
'jac' : lambda x: array([1])
},
{'type':'ineq',
'fun' : lambda x: array([x[1]]),
'jac' : lambda x: array([1])
},
{'type':'ineq',
'fun' : lambda x: array([1 - x[1]]),
'jac' : lambda x: array([-1])
});
res = scipy.optimize.minimize(
func, [3, 0.5],
args=(param.theta,-1,),
jac=func_deriv, constraints=cons,
method='SLSQP', options={'disp': True})
完整追溯:
%run "./solve_maxim.py"
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
C:\Program Files\Enthought\Canopy\App\appdata\canopy-1.1.0.1371.win-x86_64\lib\site-packages\IPython\utils\py3compat.pyc in execfile(fname, glob, loc)
174 else:
175 filename = fname
--> 176 exec compile(scripttext, filename, 'exec') in glob, loc
177 else:
178 def execfile(fname, *where):
solve_maxim.py in <module>()
61 args=(param.theta,-1,),
62 jac=func_deriv, constraints=cons,
---> 63 method='SLSQP', options={'disp': True})
AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\_minimize.pyc in minimize(fun, x0, args, method, jac, hess, hessp, bounds, constraints, tol, callback, options)
362 elif meth == 'slsqp':
363 return _minimize_slsqp(fun, x0, args, jac, bounds,
--> 364 constraints, **options)
365 else:
366 raise ValueError('Unknown solver %s' % method)
\AppData\Local\Enthought\Canopy\User\lib\site-packages\scipy\optimize\slsqp.pyc in _minimize_slsqp(func, x0, args, jac, bounds, constraints, maxiter, ftol, iprint, disp, eps, **unknown_options)
366
367 # Now combine c_eq and c_ieq into a single matrix
--> 368 c = concatenate((c_eq, c_ieq))
369
370 if mode == 0 or mode == -1: # gradient evaluation required
ValueError: all the input arrays must have same number of dimensions