100 citations · 462 across the 26 of their papers we have counts for
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An inexact proximal augmented Lagrangian framework with arbitrary linearly convergent inner solver for composite convex optimization
Fei Li, Zheng Qu
We propose an inexact proximal augmented Lagrangian framework with explicit inner problem termination rule for composite convex optimization problems. We consider arbitrary linearl…
Solving Ergodic Markov Decision Processes and Perfect Information Zero-sum Stochastic Games by Variance Reduced Deflated Value Iteration
Marianne Akian, Stéphane Gaubert, Zheng Qu +1
Recently, Sidford, Wang, Wu and Ye (2018) developed an algorithm combining variance reduction techniques with value iteration to solve discounted Markov decision processes. This al…
L-SVRG and L-Katyusha with Arbitrary Sampling
Xun Qian, Zheng Qu, Peter Richtárik
We develop and analyze a new family of {\em nonaccelerated and accelerated loopless variance-reduced methods} for finite sum optimization problems. Our convergence analysis relies…
SAGA with Arbitrary Sampling
Xu Qian, Zheng Qu, Peter Richtárik
We study the problem of minimizing the average of a very large number of smooth functions, which is of key importance in training supervised learning models. One of the most celebr…