anchor.diff:  
   With AD, this value is computed efficiently using one forward and one reverse evaluation of the function, in a matrix-free way (without computing the full Hessian matrix).
   
  -##### `AD.Laplacian(Func<D[],D>)`: Laplacian of a vector-to-scalar function
  +    [lang=csharp]
  +    // Hessian-vector product of a vector-to-scalar function
  +    var hv = AD.Hessianv(x => AD.Sin(x[0] * x[1]), new D[] { 3, 2 }, new D[] { 5, 3 });
  +
  +### AD.Laplacian
  +
  +#### Laplacian of a vector-to-scalar function
  +
  +Signature: `AD.Laplacian(Func<D[],D>)`
   
   Return value: `Func<D[],D>`
   
  
positive.code: 
  $$$
    \left( \textrm{div} \, \mathbf{F} \right)_{\mathbf{x}} = \left( \nabla \cdot \mathbf{F} \right)_{\mathbf{x}} = \textrm{tr}\left( \mathbf{J}_{\mathbf{F}} \right)_{\mathbf{x}} = \left. \left( \frac{\partial F_1}{\partial a_1} + \dots + \frac{\partial F_n}{\partial a_n}\right) \right|_{\mathbf{a}\; = \; \mathbf{x}} \; .
  
  Numerical Differentiation
  -------------------------
  
negative.code: $$$
    \mathrm{tr}\left(\mathbf{H}_f \right)_\mathbf{x} = \left. \left(\frac{\partial ^2 f}{\partial a_1^2} + \dots + \frac{\partial ^2 f}{\partial a_n^2}\right) \right|_{\mathbf{a} \; = \; \mathbf{x}} \; .
  
      [lang=csharp]
      // Laplacian of a vector-to-scalar function at a point
      var v = AD.Laplacian(x => AD.Sin(x[0] * x[1]), new D[] { 3, 2 });
  
  ### AD.Jacobian
  
  #### Jacobian of a vector-to-vector function
  
  Signature: `AD.Jacobian(Func<D[],D[]>)`
  
  Return value: `Func<D[],D[,]>`
  
  