Local LMO is a new projection-free method that achieves the convergence rates of projected gradient descent for constrained optimization by using local linear minimization oracles over small balls.
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The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.
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Local LMO: Constrained Gradient Optimization via a Local Linear Minimization Oracle
Local LMO is a new projection-free method that achieves the convergence rates of projected gradient descent for constrained optimization by using local linear minimization oracles over small balls.
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Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.