Ridge ----- The ``Ridge`` model sets up and solves the Ridge (or Tikhonov) least squares problem: $$\\text{minimize } || Ax+\\eta-b ||_2 + \\alpha || \\Delta x_i ||_2$$ $$\\text{subject to } x_i \\ge 0$$ where A is the a basis matrix, b is an observation, x is a vector of coefficients, and eta is a constant background term. Alpha is a hyperparameter that specifies the weight given to the smoothness regularization term. ``Ridge`` differs from ``Quadratic`` in that it gives defined the problem in terms of cvxpy's ``norm2()`` function, and lets it handle the appropriate factorizations. .. autoclass:: kemitter.model.Ridge :members: