Linear Convergence of Randomized Primal-Dual Coordinate Method for Large-scale Linear Constrained Convex Programming

Jul 12, 2020

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Linear constrained convex programming (LCCP) has many practical applications, including support vector machine (SVM) and machine learning portfolio (MLP) problems. We propose the randomized primal-dual coordinate (RPDC) method, a randomized coordinate extension of the first-order primal-dual method by Cohen and Zhu, 1984 and Zhao and Zhu, 2019, to solve LCCP. We randomly choose a block of variables based on the uniform distribution and apply linearization and a Bregman-like function (core function) to the selected block to obtain simple parallel primal-dual decomposition for LCCP. We establish almost surely convergence and expected O(1/t) convergence rate. Under global strong metric subregularity, we establish the linear convergence of RPDC. Both SVM and MLP problems satisfy the global strong metric subregularity condition under some reasonable conditions. Finally, we discuss the implementation details of RPDC and present numerical experiments on SVM and MLP problems to verify the linear convergence.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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