In this paper, we formulate the Generalized Convex Sizing (GCS) problem that unities and generalizes the sizing problems. We revisit the approach to solve the sizing problem by Lagrangian relaxation, point out several misunderstandings in the previous works, and extend the approach to handle general convex delay functions in the GCS problems. We identify a class of proper GCS problems whose objective functions in the simplified dual problem are differentiable and show many practical sizing problems, including the simultaneous sizing and clock skew optimization problem, are proper. We design an algorithm based on the method of feasible directions to solve proper GCS problems. The algorithm will provide evidences for infeasible GCS problems according to a condition derived by us. Experimental results confirm the efficiency and the effectiveness of our algorithm when the Elmore delay model is used.