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PPD: A Scalable and Efficient Parallel Primal-Dual Coordinate Descent Algorithm
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DOI：10.1109/TKDE.2020.3000905.png)
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Abstract
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Dual Coordinate Descent (DCD) is one of the most popular optimization methods. The parallelization of DCD is difficult, as DCD is sequential in nature. As such, simultaneously running multiple DCD threads on batches of data elements causes result inaccuracy and slow convergence, due to the concurrent updates of multiple coordinates. Some parallelization methods adopt separable approximate functions that depend on the degree of parallelism. Such dependencies result in both poor scalability and slow-convergence. To address these challenges, in this paper we present a new parallel primal-dual algorithm for DCD, called PPD. In PPD, the block data distribution is utilized to obtain a new approximate function that is independent from the parallelism. Moreover, PPD is designed with a novel primal-dual acceleration scheme to approach the optimal solution closely and quickly. We demonstrate the advantages of PPD in terms of scalability and efficiency through experiments.
Keywords:
Optimization
Approximation algorithms
Convergence
Parallel processing
Partitioning algorithms
Scalability
Message systems
Parallel computing
dual coordinate descent
convex optimization
classification
machine learning
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