In recent years, the emergence of a large number of parallel data makes us pay more and more attention to how to improve the efficiency of decision-making. In this paper, scalable machine learning is the research object. First, the existing problems and development directions of existing related technologies are analyzed. Then, an extensible machine learning algorithm is introduced to solve these problems. Finally, MATLAB is used to realize the optimization simulation of n subsystems, and the regression equation between the final result and the optimal value as well as the mean square error is obtained. The corresponding conclusions are drawn. The parallel and distributed optimization algorithms of extensible machine learning run for a short time, The parallel capacity is about 3500k, and the global performance can be effectively improved by updating existing methods in the feasible region.

Xin Wang. Parallel and Distributed Optimization Algorithms for Scalable Machine Learning. Machine Learning Theory and Practice (2022), Vol. 3, Issue 3: 43-51. https://doi.org/10.38007/ML.2022.030306.

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