The “cutting plane” method of Kelley for nonlinear programming problems applies linear programming, through a sequence of local linearizations, to the problem of minimizing a convex function of real variables subject to linear inequality constraints. A procedure is presented here for improving the constructed linearizations which may considerably accelerate the convergence of the process. In the case of a quadratic objective function satisfying certain mild conditions this improvement yields a finite algorithm.