Asymptotic linear convergence of fully-corrective generalized conditional gradient methods

Abstract

We propose a fully-corrective generalized conditional gradient method (FC-GCG) for the minimization of the sum of a smooth, convex loss function and a convex one-homogeneous regularizer over a Banach space. The algorithm relies on the mutual update of a finite set $$\mathcal {A}_k$$ A k of extremal points of the unit ball of the regularizer and of an iterate $$u_k \in {\text {cone}}(\mathcal {A}_k)$$ u k ∈ cone ( A k ) . Each iteration requires the solution of one linear problem to update $$\mathcal {A}_k$$ A k and of one finite dimensional convex minimization problem to update the iterate. Under standard hypotheses on the minimization problem we show that the algorithm converges sublinearly to a solution. Subsequently, imposing additional assumptions on the associated dual variables, this is improved to a linear rate of convergence. The proof of both results relies on two key observations: First, we prove the equivalence of the considered problem to the minimization of a lifted functional over a particular space of Radon measures using Choquet’s theorem. Second, the FC-GCG algorithm is connected to a Primal-Dual-Active-Point method on the lifted problem for which we finally derive the desired convergence rates.