 In some contexts a regularized version of the least squares solution may be preferable.Tikhonov regularization (or ridge regression) adds a constraint that formula_60, the L2-norm of the parameter vector, is not greater than a given value.Equivalently, it may solve an unconstrained minimization of the least-squares penalty with formula_61 added, where formula_62 is a constant (this is the Lagrangian form of the constrained problem).