For example, the least-squares method can be viewed as a simple form of regularization, if the regularization term λ R ( f ) {\displaystyle \lambda R(f)} is dropped from the optimization problem (i.e. λ = 0 {\displaystyle \lambda =0} ) and the loss function summation of V ( f ( x i ) , y i ) {\displaystyle V(f(x_{i}),y_{i})} is the L 2 {\displaystyle L_{2}} norm of the difference between the fitted and actual function values (i.e. ‖ Y − f ( X ) ‖ 2 {\displaystyle \|Y-f(X)\|_{2}} ). A simple form of regularization applied to integral equations, generally termed Tikhonov regularization after Andrey Nikolayevich Tikhonov, is essentially a trade-off between fitting the data and reducing a norm of the solution. More recently, non-linear regularization methods, including total variation regularization, have become popular.