AN INTERIOR-POINT METHOD FOR SEPARABLE CONVEX OPTIMIZATION Separable convex optimization generalizes many well-studied classes of convex optimization such as linear optimization, quadratically constrained quadratic optimization, geometric optimization, etc. We apply the classical interior-point methodology to these problems using a conic formulation. In particular, we investigate how the general notions of complementarity conditions, central path, self-concordance and short-step method can be specialized and studied in this context.