make_log_hessian: Make Hessian of the objective function
In rmsharp/stochasticreserver: Flexible Framework for Stochastic Reserving Models

- m_expected_value is the expectated value matrix - m_variance is the matrix of variances - A, m_expected_value, m_variance all have shape c(size, size) - The variables _v are copies of the originals to shape c(npar,size,size), paralleling the gradient of g. - The variables _m are copies of the originals to shape c(npar,npar,size,size), paralleling the hessian of g hessian-of-objective-function

1	make_log_hessian(a, A, dnom, g_obj, g_grad, g_hess)

`a`	do not know
`A`	do not know
`dnom`	numeric vector representing the exposures (claims) used in the denominator
`g_obj`	objective function
`g_grad`	gradient function
`g_hess`	hessian function