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Convex composition optimization is an emerging topic that covers a wide range of applications arising from stochastic optimal control, reinforcement learning and multi-stage stochastic programming.
Some NP-complete problems in quadratic and nonlinear programming
K. G. Murty and S. N. Kabadi · 1987
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Neuro-dynamic programming: an overview
D. P. Bertsekas and J. N. Tsitsiklis · 1995
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Reinforcement Learning: An Introduction
R. S. Sutton and A. G. Barto · 1998
Earlier work this paper cites.
Sparse additive models
P. Ravikumar, J. Lafferty, H. Liu, and L. Wasserman · 2009
Earlier work this paper cites.
Lectures on Stochastic Programming: Modeling and Theory
A. Shapiro, D. Dentcheva, and A. Ruszczyński · 2009
Earlier work this paper cites.
Introductory Lectures on Convex Optimization: A Basic Course
Y. Nesterov · 2013
Cited alongside, same era.
Proximal algorithms
N. Parikh and S. Boyd · 2014
Cited alongside, same era.
Improved SVRG for non-strongly-convex or sum-of-non-convex objectives
Z. Allen-Zhu and Y. Yuan · 2016
Cited alongside, same era.
SDCA without duality, regularization, and individual convexity
S. Shalev-Shwartz · 2016
Cited alongside, same era.
Finite-sum composition optimization via variance reduced gradient descent
X. Lian, M. Wang, and J. Liu · 2017
Cited alongside, same era.
Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
M. Wang, E. X. Fang, and H. Liu · 2017
Later among the works it cites.
Accelerating stochastic composition optimization
M. Wang, J. Liu, and E. X. Fang · 2017
Later among the works it cites.
Fast stochastic variance reduced admm for stochastic composition optimization
Y. Yu and L. Huang · 2017
Later among the works it cites.
Accelerated method for stochastic composition optimization with nonsmooth regularization
Z. Huo, B. Gu, J. Liu, and H. Huang · 2018
Closest in time.
High-dimensional Statistics: A Non-asymptotic Viewpoint
M. J. Wainwright · 2019
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