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Using Experimental`NumericalFunction frameworkExperimental`NumericalFunction framework directly (FindMinimum uses it under the hoodFindMinimum uses it under the hood) it is straightforward to get the numerical approximation of the Hessian:

f = Experimental`CreateNumericalFunction[{x, y}, 
   Cos[x^2 - 3 y] + Sin[x^2 + y^2], {1}, Hessian -> FiniteDifference];

f["Hessian"[{1.376384972443001`, 1.6786760817546214`}]]

{{{15.1555, 0.983708}, {0.983708, 20.2718}}}

Using Experimental`NumericalFunction framework directly (FindMinimum uses it under the hood) it is straightforward to get the numerical approximation of the Hessian:

f = Experimental`CreateNumericalFunction[{x, y}, 
   Cos[x^2 - 3 y] + Sin[x^2 + y^2], {1}, Hessian -> FiniteDifference];

f["Hessian"[{1.376384972443001`, 1.6786760817546214`}]]

{{{15.1555, 0.983708}, {0.983708, 20.2718}}}

Using Experimental`NumericalFunction framework directly (FindMinimum uses it under the hood) it is straightforward to get the numerical approximation of the Hessian:

f = Experimental`CreateNumericalFunction[{x, y}, 
   Cos[x^2 - 3 y] + Sin[x^2 + y^2], {1}, Hessian -> FiniteDifference];

f["Hessian"[{1.376384972443001`, 1.6786760817546214`}]]

{{{15.1555, 0.983708}, {0.983708, 20.2718}}}

1
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Using Experimental`NumericalFunction framework directly (FindMinimum uses it under the hood) it is straightforward to get the numerical approximation of the Hessian:

f = Experimental`CreateNumericalFunction[{x, y}, 
   Cos[x^2 - 3 y] + Sin[x^2 + y^2], {1}, Hessian -> FiniteDifference];

f["Hessian"[{1.376384972443001`, 1.6786760817546214`}]]

{{{15.1555, 0.983708}, {0.983708, 20.2718}}}