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In this paper, we explore two fundamental first-order algorithms in convex optimization, namely, gradient descent (GD) and proximal gradient method (ProxGD).
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A.. Goldstein · 1962
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“Minimization of functions having Lipschitz continuous first partial derivatives”
Larry Armijo · 1966
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“Two-point step size gradient methods”
Jonathan Barzilai and Jonathan. Borwein · 1988
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“Convex Optimization”
Stephen Boyd and Lieven Vandenberghe · 2004
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“A first-order primal-dual algorithm for convex problems with applications to imaging”
Antonin Chambolle and Thomas Pock · 2010
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“Adaptive bound optimization for online convex optimization”
H. McMahan and Matthew Streeter · 2010
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“An elementary approach to tight worst case complexity analysis of gradient based methods”
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