Abstract. We propose a new version of the Frank–Wolfe method, called the -Frank–Wolfe algorithm, for optimization problems with -smooth objectives. We demonstrate that this algorithm achieves superior theoretical convergence rates compared to the classical Frank–Wolfe method. In addition, we introduce a novel adaptive procedure, termed the Adaptive -Frank–Wolfe algorithm, which dynamically adjusts the smoothness parameters to further improve performance and stability. Comprehensive numerical experiments confirm the theoretical results and demonstrate the clear practical advantages of both proposed algorithms over existing Frank–Wolfe variants.
How to Cite this Article:
A. A. Vyguzov, F. S. Stonyakin, A. V. Gasnikov, Frank-Wolfe algorithms for -smooth functions, J. Nonlinear Var. Anal. 10 (2026), 597-616.
