Generalizing the Optimized Gradient Method for Smooth Convex Minimization
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Publication:4571883
DOI10.1137/17M112124XzbMath1400.90245arXiv1607.06764WikidataQ129601844 ScholiaQ129601844MaRDI QIDQ4571883
Jeffrey A. Fessler, Donghwan Kim
Publication date: 3 July 2018
Published in: SIAM Journal on Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1607.06764
Analysis of algorithms and problem complexity (68Q25) Semidefinite programming (90C22) Convex programming (90C25) Abstract computational complexity for mathematical programming problems (90C60) Nonlinear programming (90C30) Discrete approximations in optimal control (49M25)
Related Items (14)
Accelerated proximal algorithms with a correction term for monotone inclusions ⋮ Adaptive restart of the optimized gradient method for convex optimization ⋮ Fast proximal algorithms for nonsmooth convex optimization ⋮ Primal–dual accelerated gradient methods with small-dimensional relaxation oracle ⋮ Potential Function-Based Framework for Minimizing Gradients in Convex and Min-Max Optimization ⋮ Fast gradient methods for uniformly convex and weakly smooth problems ⋮ Factor-\(\sqrt{2}\) acceleration of accelerated gradient methods ⋮ Another Look at the Fast Iterative Shrinkage/Thresholding Algorithm (FISTA) ⋮ Operator Splitting Performance Estimation: Tight Contraction Factors and Optimal Parameter Selection ⋮ Tight Sublinear Convergence Rate of the Proximal Point Algorithm for Maximal Monotone Inclusion Problems ⋮ Accelerated proximal point method for maximally monotone operators ⋮ Optimizing the efficiency of first-order methods for decreasing the gradient of smooth convex functions ⋮ Nearly optimal first-order methods for convex optimization under gradient norm measure: an adaptive regularization approach ⋮ Generalized Momentum-Based Methods: A Hamiltonian Perspective
Uses Software
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