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Helper functions
Fitting functions :
NB : the first dimension of the cost, gradient or hessian is the number of points to fit, the second is the dimension of the point if there is one. This leads to the fact that the gradient returns in fact the jacobian of the function.
Finite Difference functions :
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Quadratic Defines a cost function with a quadratic cost |
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CenteredFiniteDifferences A function that will be able to computes its derivatives with a centered difference formula |
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ForwardFiniteDifferencesCache A function that will be able to computes its derivatives with a forward difference formula. |
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ForwardFiniteDifferences A function that will be able to computes its derivatives with a forward difference formula |
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FiniteDifferencesFunction |
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helpers__all__ =
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helpers__all__
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