Projection-Free Adaptive Gradients for Large-Scale Optimization
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The complexity in large-scale optimization can lie in both handling the objective function and handling the constraint set. In this respect, stochastic Frank-Wolfe algorithms occupy a unique position as they alleviate both computational burdens, by querying only approximate first-order information from the objective and by maintaining feasibility of the iterates without using projections. In this paper, we improve the quality of their first-order information by blending in adaptive gradients. Starting from the design of adaptive gradient algorithms, we propose to solve the occurring constrained optimization subproblems very incompletely via a fixed and small number of iterations of the Frank-Wolfe algorithm (often times only 2 iterations), in order to preserve the low per-iteration complexity. We derive convergence rates and demonstrate the computational advantage of our method over the state-of-the-art stochastic Frank-Wolfe algorithms.
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