I have a function of four input variables which I am trying to minimize using the Levenburg-Marquardt optimization method. The previous results where the Hessian/Gradient is calculated using Forward Difference Approximation wasn't accurate enough so, I wanted to add the Hessian/Gradient as a callable argument to the least_squares() method. This is what I have tried --
Using Sympy, I calculated the gradient and the Hessian using
gradient_vec = [diff(obj_func, var) for var in (x1, x2, y1, y2)] hessian_mat = [[obj_func.diff(var1).diff(var2) for var1 in list((x1, x2, y1, y2))] for var2 in list((x1, x2, y1, y2))] grad_func = lambdify([x1, x2, y1, y2, f], gradient_vec, 'numpy') hess_matr_func = lambdify([x1, x2, y1, y2, f], hessian_mat, 'numpy')
f is an additional argument to both the gradient and hessian functions.
leastsq function call I have (my objective function has only one input),
result = leastsq(obj_fun, x0=np.random.uniform(size=(4,)), Dfun=grad_func, args=(f,))
I run this and I keep getting this error
TypeError: obj_fun() takes 1 positional argument but 2 were given
So, I tried the
least_squares() function with
method='lm' argument and when I pass the Hessian as,
result = least_squares(obj_fun, x0=np.random.uniform(size=(4,), method='lm', jac=hess_matr_func, args=(f,))
And I still get the same error. How do I pass an argument
*args but to the Gradient/Hessian callables alone? I tried using the
functools.partial to create a wrapper around the callable function and even that didn't help.
Thanks very much for your help!