STRONG DUALITY FOR GEOMETRIC OPTIMIZATION AND L_P-NORM OPTIMIZATION USING A
CONIC FORMULATION

Geometric optimization can be cast as a convex problem with an interesting
strong duality property : the optimum duality gap is equal to zero. l_p-norm
optimization problems also share this property, with the addition that their
dual optimum objective value is attained at a feasible solution.

In this talk, we formulate these problems as primal-dual pairs using dedicated
convex cones, which allow us to derive these strong duality properties rather
easily.