KLApproxDiagonalInferenceMethod (the third KL method)

@karlnapf 
Take a look at this.
Note that it may have similar issues you mentioned in other KL methods.
I will correct all of them tomorrow.
Just have it run to ensure everything work well.
I know from experience that kernel matrices quickly end up being numerically not psd (very small negative Eigenvalues) In particular when they grow large: For example for Gaussian kernel, the Eigenvalues decay with an exponential rate, which is problematic. Adding this ridge usually helps. Why don't you check for this here and add if necessary? Also, usually tried to avoid computing Cholesky of the kernel matrix itself, but of kernel + noise on diagonal. In this case, we can safely assume that its psd.
Thoughts?