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For deterministic optimization, line-search methods augment algorithms by providing stability and improved efficiency.
A stochastic approximation method
Herbert Robbins and Sutton Monro · 1951
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Minimization of functions having lipschitz continuous first partial derivatives
Larry Armijo · 1966
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Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming
Abraham P. George and Warren B. Powell · 2006
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Numerical Optimization
J. Nocedal and S. J. Wright · 2006
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Adaptive subgradient methods for online learning and stochastic optimization
J. C. Duchi, E. Hazan, and Y. Singer · 2011
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Sample size selection in optimization methods for machine learning
R. Byrd, G. M. Chin, J. Nocedal, and Y. Wu · 2012
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Hybrid deterministic-stochastic methods for data fitting
M Friedlander and M. Schmidt · 2012
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Fast probabilistic optimization from noisy gradients
Philipp Hennig · 2013
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Convergence of trust-region methods based on probabilistic models
A. S. Bandeira, K. Scheinberg, and L. N. Vicente · 2014
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On adaptive sampling rules for stochastic recursions
Fatemeh S Hashemi, Soumyadip Ghosh, and Raghu Pasupathy · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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An introduction to matrix concentration inequalities
Joel A. Tropp · 2015
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Convergence Rate Analysis of a Stochastic Trust Region Method for Nonconvex Optimization
Adaptive sampling strategies for stochastic optimization
Raghu Bollapragada, Richard Byrd, and Jorge Nocedal · 2017
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Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
C. Cartis and K. Scheinberg · 2017
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Stochastic optimization using a trust-region method and random models
R. Chen, M. Menickelly, and K. Scheinberg · 2017
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Probabilistic line searches for stochastic optimization
Maren Mahsereci and Philipp Hennig · 2017
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Sub-sampled newton methods i: Globally convergent algorithms
Farbod Roosta-Khorasani and Michael W. Mahoney · 2017
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J. Blanchet, C. Cartis, M. Menickelly, and K. Scheinberg · 2017
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