{"ID":2891386,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.17545","arxiv_id":"2507.17545","title":"Scalable DC Optimization via Adaptive Frank-Wolfe Algorithms","abstract":"We consider the problem of minimizing a difference of (smooth) convex functions over a compact convex feasible region $P$, i.e., $\\min_{x \\in P} f(x) - g(x)$, 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.","short_abstract":"We consider the problem of minimizing a difference of (smooth) convex functions over a compact convex feasible region $P$, i.e., $\\min_{x \\in P} f(x) - g(x)$, with smooth $f$ and Lipschitz continuous $g$. This computational study builds upon and complements the framework of Maskan et al. [2025] by integrating advanced...","url_abs":"https://arxiv.org/abs/2507.17545","url_pdf":"https://arxiv.org/pdf/2507.17545v2","authors":"[\"Sebastian Pokutta\"]","published":"2025-07-23T14:22:42Z","proceeding":"math.OC","tasks":"[\"math.OC\",\"cs.LG\"]","methods":"[]","has_code":false}