Estimate Of Inverse Hessian Using Scipy Minimization

I am using SciPy's 'minimize' function to minimize a function. The function returns the optimal value, along with an estimated Jacobian and Hessian. As below: fun: -675.09792378630

Solution 1:

From the SciPy documentation for the LbfgsInvHessProduct; you can use the method todense() to obtain the LbfgsInvHessProduct's values as a dense array.

However, keep in mind the LbfgsInvHessProduct is still a matrix! It's a special memory-optimized format, but you can still call other matrix functions such as matmat(), transpose(), dot() etc.