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Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization

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Figure 1. The comparison of the simulated and the real implemen- implemen-tation of as-L-BFGS with W = 40 workers.
Figure 3. The required time to achieve ε-accuracy by as-L-BFGS for increasing number of workers
Figure 4 shows the performance of the three algorithms on the MovieLens datasets in terms of the  root-mean-squared-error (RMSE), which is a standard metric for  recommenda-tion systems, and the norm of the gradients through  itera-tions
Figure S1. The convergence behavior of as-L-BFGS on the ML-1M dataset for increasing number of workers.
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