Hessian Scaling
The rows and columns of the Hessian matrix
can be scaled when you use the trust region, NewtonRaphson,
and double dogleg optimization techniques.
Each element H_{i,j}, i,j = 1, ... ,n is divided by the
scaling factor d_{i} d_{j}, where the scaling vector
d = (d_{1}, ... ,d_{n}) is iteratively updated in a way
specified by the HESCAL=i option, as follows.
 i = 0:
 No scaling is done (equivalent to d_{i}=1).

 First iteration and each restart iteration sets:
 i = 1:
 Refer to Mor (1978):
 i = 2:
 Refer to Dennis, Gay, and Welsch (1981):
 i = 3:
 d_{i} is reset in each iteration:
In the preceding equations, is the relative machine
precision or, equivalently, the largest double precision value that,
when added to 1, results in 1.
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