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Found 4 papers, 2 papers with code
Self-concordant analysis of Frank-Wolfe algorithm
Projection-free optimization via different variants of the Frank-Wolfe (FW) method has become one of the cornerstones in optimization for machine learning since in many cases the linear minimization oracle is much cheaper to implement than projections and some sparsity needs to be preserved.
AdaBatchGrad: Combining Adaptive Batch Size and Adaptive Step Size
To substantiate the efficacy of our method, we experimentally show, how the introduction of adaptive step size and adaptive batch size gradually improves the performance of regular SGD.
Tensor methods for strongly convex strongly concave saddle point problems and strongly monotone variational inequalities
The first method is based on the assumption of $p$-th order smoothness of the objective and it achieves a convergence rate of $O \left( \left( \frac{L_p R^{p - 1}}{\mu} \right)^\frac{2}{p + 1} \log \frac{\mu R^2}{\varepsilon_G} \right)$, where $R$ is an estimate of the initial distance to the solution, and $\varepsilon_G$ is the error in terms of duality gap.
Optimization and Control
Self-Concordant Analysis of Frank-Wolfe Algorithms
Projection-free optimization via different variants of the Frank-Wolfe (FW), a. k. a.
