[英]defining the Hessian as zero
While using scipy.optimize.minimize with the trust-constr method I got this UserWarning: 在通过trust-constr方法使用scipy.optimize.minimize时,我得到了以下UserWarning:
scipy\optimize\_hessian_update_strategy.py:187: UserWarning: delta_grad == 0.0. Check if the approximated function is linear. If the function is linear better results can be obtained by defining the Hessian as zero instead of using quasi-Newton approximations. 'approximations.', UserWarning)
I have a linear function so I want to try to set the hessian as zero. 我有一个线性函数,所以我想尝试将hessian设置为零。 But how does this work?
但这如何工作? I tried the easiest way with "hess = None" as parameter.
我尝试了以“ hess = None”作为参数的最简单方法。 Okay, a bad try.
好吧,不好尝试。
This is the line calling the solver: 这是调用求解器的行:
solution = scopt.minimize(minimizeFunction,initialGuess ,method='trust-constr', constraints=cons,options={'disp':True,'verbose':3},bounds=bnds)
When you define the constraint, you want to set 定义约束时,要设置
hess = lambda x, v: numpy.zeros((n, n))
Here n
is the dimension of the array. 这里
n
是数组的维数。 Notice that you can also use a LinearConstraint object 请注意,您还可以使用LinearConstraint对象
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