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Iterative first-order methods such as gradient descent and its variants are widely used for solving optimization and machine learning problems.
Introduction to optimization
B. T. Polyak · 1987
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A collection of robust control problems leading to LMIs
A. Packard, K. Zhou, P. Pandey, and G. Becker · 1991
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Linear matrix inequalities in system and control theory
S. P. Boyd, L. El Ghaoui, E. Feron, and V. Balakrishnan · 1994
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A linear matrix inequality approach to
P. Gahinet and P. Apkarian · 1994
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Introductory lectures on convex optimization: A basic course
Y. Nesterov · 2004
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A general analysis of the convergence of ADMM
R. Nishihara, L. Lessard, B. Recht, A. Packard, and M. I. Jordan · 2015
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Stability analysis with dissipation inequalities and integral quadratic constraints
P. Seiler · 2015
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Zames–Falb multipliers for absolute stability: From O’Shea’s contribution to convex searches
J. Carrasco, M. C. Turner, and W. P. Heath · 2016
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Exponential decay rate conditions for uncertain linear systems using integral quadratic constraints
B. Hu and P. Seiler · 2016
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Analysis and design of optimization algorithms via integral quadratic constraints
L. Lessard, B. Recht, and A. Packard · 2016
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Exponential stability analysis via integral quadratic constraints, 2017
R. Boczar, L. Lessard, A. Packard, and B. Recht · 2017
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Dissipativity theory for Nesterov’s accelerated method
B. Hu and L. Lessard · 2017
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Robust convergence analysis of distributed optimization algorithms
A. Sundararajan, B. Hu, and L. Lessard · 2017
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A robust accelerated optimization algorithm for strongly convex functions
S. Cyrus, B. Hu, B. V. Scoy, and L. Lessard · 2018
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Noncausal Zames–Falb multipliers for tighter estimates of exponential convergence rates
R. Freeman · 2018
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The fastest known globally convergent first-order method for minimizing strongly convex functions
B. Van Scoy, R. A. Freeman, and K. M. Lynch · 2018
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Control interpretations for first-order optimization methods
B. Hu and L. Lessard · 2017
Cited alongside, same era.