This paper will demonstrate how the convexity and quadratic nature of the soft constrained model predictive control problem can be used to solve for its unique minimum in a finite number of steps. A mathematical formulation for this problem will be given that leads to a new convergent minimization algorithm. This algorithm will then be compared to a traditional method of steepest descent type algorithm in an example.