The paper proposes an auto-conditioned framework for Frank-Wolfe algorithms that replaces global smoothness constants with local estimators computed from first-order information, achieving convergence to stationary points in nonconvex settings and sublinear rates in convex settings without prior kno
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Auto-Conditioned Frank-Wolfe Algorithms
The paper proposes an auto-conditioned framework for Frank-Wolfe algorithms that replaces global smoothness constants with local estimators computed from first-order information, achieving convergence to stationary points in nonconvex settings and sublinear rates in convex settings without prior kno