Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization

• URL: http://arxiv.org/abs/2304.11665v1
• Date: Sun, 23 Apr 2023 14:21:29 GMT
• Title: Accelerated Doubly Stochastic Gradient Algorithm for Large-scale Empirical Risk Minimization
• Authors: Zebang Shen, Hui Qian, Tongzhou Mu, Chao Zhang
• Abstract summary: We propose a doubly algorithm with a novel accelerating multimomentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and updates a small block of variable coordinates.
• Score: 23.271890743506514
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Nowadays, algorithms with fast convergence, small memory footprints, and low per-iteration complexity are particularly favorable for artificial intelligence applications. In this paper, we propose a doubly stochastic algorithm with a novel accelerating multi-momentum technique to solve large scale empirical risk minimization problem for learning tasks. While enjoying a provably superior convergence rate, in each iteration, such algorithm only accesses a mini batch of samples and meanwhile updates a small block of variable coordinates, which substantially reduces the amount of memory reference when both the massive sample size and ultra-high dimensionality are involved. Empirical studies on huge scale datasets are conducted to illustrate the efficiency of our method in practice.

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