Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms

We consider the problem of minimizing a difference of (smooth) convex functions over a compact convex feasible region P, i.e., minx ∈ P f(x) - g(x), with smooth f and Lipschitz continuous g. This computational study builds upon and complements the framework of Maskan et al. [2025] by integrating advanced Frank-Wolfe variants to reduce computational overhead. We empirically show that constrained DC problems can be efficiently solved using a combination of the Blended Pairwise Conditional Gradients (BPCG) algorithm [Tsuji et al., 2022] with warm-starting and the adaptive error bound from Maskan et al. [2025]. The result is a highly efficient and scalable projection-free algorithm for constrained DC optimization.