EmpiricalHessian

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11. def hessian(df: DiffFunction[DenseVector[Double]], x: DenseVector[Double], eps: Double = 1E-5)(implicit vs: VectorSpace[DenseVector[Double], Double], copy: CanCopy[DenseVector[Double]]): DenseMatrix[Double]

    

    Calculate the Hessian using central differences

    Calculate the Hessian using central differences

    H_{i,j} = \lim_h -> 0 ((f'(x_{i} + h*e_{j}) - f'(x_{i} + h*e_{j}))/4*h + (f'(x_{j} + h*e_{i}) - f'(x_{j} + h*e_{i}))/4*h)

    where e_{i} is the unit vector with 1 in the i^^th position and zeros elsewhere

    df

    differentiable function

    x

    the point we compute the hessian for

    eps

    a small value

    returns

    Approximate hessian matrix

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16. implicit def product[T, I]: linalg.operators.OpMulMatrix.Impl2[EmpiricalHessian[T], T, T]

    

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