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This work proposes an accelerated first-order algorithm we call the Robust Momentum Method for optimizing smooth strongly convex functions.
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M. Schmidt, N. L. Roux, and F. R. Bach, “Convergence rates of inexact proximal-gradient methods for convex optimization,” in
2011
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——, “Intermediate gradient methods for smooth convex problems with inexact oracle,” CORE Discussion Paper 2013/17, Tech. Rep., 2013
2013
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O. Devolder, F. Glineur, and Y. Nesterov, “First-order methods of smooth convex optimization with inexact oracle,”
2014
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R. Boczar, L. Lessard, and B. Recht, “Exponential convergence bounds using integral quadratic constraints,” in
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L. Lessard, B. Recht, and A. Packard, “Analysis and design of optimization algorithms via integral quadratic constraints,”
2016
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B. Hu and L. Lessard, “Dissipativity theory for Nesterov’s accelerated method,” in
2017
Closest in time.
B. Hu, P. Seiler, and A. Rantzer, “A unified analysis of stochastic optimization methods using jump system theory and quadratic constraints,” in
2017
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E. de Klerk, F. Glineur, and A. B. Taylor, “On the worst-case complexity of the gradient method with exact line search for smooth strongly convex functions,”
2017
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2017
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B. Van Scoy, R. A. Freeman, and K. M. Lynch, “The fastest known globally convergent first-order method for minimizing strongly convex functions,”
2018
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