1,178 research outputs found
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization
Stochastic composition optimization draws much attention recently and has
been successful in many emerging applications of machine learning, statistical
analysis, and reinforcement learning. In this paper, we focus on the
composition problem with nonsmooth regularization penalty. Previous works
either have slow convergence rate or do not provide complete convergence
analysis for the general problem. In this paper, we tackle these two issues by
proposing a new stochastic composition optimization method for composition
problem with nonsmooth regularization penalty. In our method, we apply variance
reduction technique to accelerate the speed of convergence. To the best of our
knowledge, our method admits the fastest convergence rate for stochastic
composition optimization: for strongly convex composition problem, our
algorithm is proved to admit linear convergence; for general composition
problem, our algorithm significantly improves the state-of-the-art convergence
rate from to . Finally, we apply
our proposed algorithm to portfolio management and policy evaluation in
reinforcement learning. Experimental results verify our theoretical analysis.Comment: AAAI 201
Correlated Quantization for Faster Nonconvex Distributed Optimization
Quantization (Alistarh et al., 2017) is an important (stochastic) compression
technique that reduces the volume of transmitted bits during each communication
round in distributed model training. Suresh et al. (2022) introduce correlated
quantizers and show their advantages over independent counterparts by analyzing
distributed SGD communication complexity. We analyze the forefront distributed
non-convex optimization algorithm MARINA (Gorbunov et al., 2022) utilizing the
proposed correlated quantizers and show that it outperforms the original MARINA
and distributed SGD of Suresh et al. (2022) with regard to the communication
complexity. We significantly refine the original analysis of MARINA without any
additional assumptions using the weighted Hessian variance (Tyurin et al.,
2022), and then we expand the theoretical framework of MARINA to accommodate a
substantially broader range of potentially correlated and biased compressors,
thus dilating the applicability of the method beyond the conventional
independent unbiased compressor setup. Extensive experimental results
corroborate our theoretical findings
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