Random-Subspace Frank--Wolfe over Strongly Convex Sets

Frank--Wolfe methods avoid projections, but over curved feasible regions the full-space linear minimization oracle (LMO) can itself become the computational bottleneck. We introduce random-subspace Frank--Wolfe (RSFW), the first Frank--Wolfe framework, to our knowledge, that replaces the ambient LMO by exact LMOs over random low-dimensional affine sections of a general feasible set, while preserving feasibility in the original space. For smooth convex objectives over compact strongly convex feasible sets, we prove a dimension-explicit approximate-oracle inequality and derive the standard $O(1/k)$ open-loop rate, with high-probability and almost-sure counterparts. Under short steps and a gradient lower bound, the same geometric control yields linear convergence, and we extend the sublinear theory to finite-sum stochastic gradients. We also show that random sections can improve the local curvature model controlling short steps: for smooth objectives, the quadratic model along a sampled section is governed by the compressed Hessian, yielding computable $d\times d$ curvature constants for quadratic objectives over balls and ellipsoids. These results provide a geometric theory of oracle-side randomization in projection-free optimization.