Abstract: Training machine learning models often involves solving high-dimensional stochastic optimization problems, where stochastic gradient-based algorithms are hindered by slow convergence.
Abstract: In this paper, various algorithmic models such as K-means clustering, BPR model, Dijkstra algorithm, Fruchterman-Reingold algorithm, and Frank-Wolfe algorithm are proposed, focusing on the ...
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