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An adaptive regularization algorithm using inexact function and derivatives evaluations is proposed for the solution of composite nonsmooth nonconvex optimization.
Conditions for convergence of trust region algorithms for nonsmooth optimization
Y. Yuan · 1985
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Numerical experience with a class of algorithms for nonlinear optimization using inexact function and gradient information
R. G. Carter · 1993
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Regression shrinkage and selection via the LASSO
R. Tibshirani · 1996
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Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion
P. C. Hansen · 1998
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Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Trust-Region Methods
A. R. Conn, N. I. M. Gould, and Ph. L. Toint · 2000
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Convex Optimization
S. Boyd and L. Vandenberghe · 2004
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Introductory Lectures on Convex Optimization
Yu. Nesterov · 2004
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Compressed sensing
D. L. Donoho · 2006
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Recursive trust-region methods for multiscale nonlinear optimization
S. Gratton, A. Sartenaer, and Ph. L. Toint · 2008
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A fast iterative shrinkage-thresholding algorithm for linear inverse problems
A. Beck and M. Teboulle · 2009
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On the evaluation complexity of composite function minimization with applications to nonconvex nonlinear programming
C. Cartis, N. I. M. Gould, and Ph. L. Toint · 2011
Cited alongside, same era.
On the oracle complexity of first-order and derivative-free algorithms for smooth nonconvex minimization
C. Cartis, N. I. M. Gould, and Ph. L. Toint · 2012
Cited alongside, same era.
A proximal method for composite minimization
A. S. Lewis and S. J. Wright · 2016
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Proximal stochastic methods for nonsmooth nonconvex finite-sum optimization
S. Reddi, S. Sra, B. Póczos, and A. Smola · 2016
Cited alongside, same era.
Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models
A subsampling line-search method with second-order results
E. Bergou, Y. Diouane, V. Kungurtsev, and C. W. Royer · 2018
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Convergence rate analysis of a stochastic trust region method via supermartingales
J. Blanchet, C. Cartis, M. Menickelly, and K. Scheinberg · 2018
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C. Cartis, N. I. M. Gould, and Ph. L. Toint · 2018
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Global convergence rate analysis of unconstrained optimization methods based on probabilistic models
C. Cartis and K. Scheinberg · 2018
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On adaptive cubic regularization Newton’s methods for convex optimization via random sampling
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E. G. Birgin, J. L. Gardenghi, J. M. Martínez, S. A. Santos, and Ph. L. Toint · 2017
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Newton-type methods for non-convex optimization under inexact Hessian information
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Stochastic methods for composite and weakly convex optimization problems
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A note on solving nonlinear optimization problems in variable precision
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Stochastic second-order methods for non-convex optimization with inexact Hessian and gradient
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Training deep neural networks with 8-bit floating point numbers
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