An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity
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[1] Serge Gratton,et al. A note on solving nonlinear optimization problems in variable precision , 2018, Computational Optimization and Applications.
[2] Daniel Brand,et al. Training Deep Neural Networks with 8-bit Floating Point Numbers , 2018, NeurIPS.
[3] Michael W. Mahoney,et al. Sub-sampled Newton methods , 2018, Math. Program..
[4] S. Bellavia,et al. Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization , 2018, SIAM J. Optim..
[5] Nicholas I. M. Gould,et al. Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraints , 2018, SIAM J. Optim..
[6] Vyacheslav Kungurtsev,et al. A Subsampling Line-Search Method with Second-Order Results , 2018, INFORMS J. Optim..
[7] Cho-Jui Hsieh,et al. Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient , 2018, ArXiv.
[8] S. Bellavia,et al. Theoretical study of an adaptive cubic regularization method with dynamic inexact Hessian information , 2018 .
[9] Dmitriy Drusvyatskiy,et al. Stochastic model-based minimization of weakly convex functions , 2018, SIAM J. Optim..
[10] Tianyi Lin,et al. On Adaptive Cubic Regularized Newton's Methods for Convex Optimization via Random Sampling , 2018 .
[11] Peng Xu,et al. Newton-type methods for non-convex optimization under inexact Hessian information , 2017, Math. Program..
[12] José Mario Martínez,et al. Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models , 2017, Math. Program..
[13] Feng Ruan,et al. Stochastic Methods for Composite and Weakly Convex Optimization Problems , 2017, SIAM J. Optim..
[14] Alexander J. Smola,et al. Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization , 2016, NIPS.
[15] Katya Scheinberg,et al. Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales , 2016, INFORMS Journal on Optimization.
[16] Warren Hare,et al. A proximal bundle method for nonsmooth nonconvex functions with inexact information , 2015, Computational Optimization and Applications.
[17] Katya Scheinberg,et al. Global convergence rate analysis of unconstrained optimization methods based on probabilistic models , 2015, Mathematical Programming.
[18] Matthias Heinkenschloss,et al. Inexact Objective Function Evaluations in a Trust-Region Algorithm for PDE-Constrained Optimization under Uncertainty , 2014, SIAM J. Sci. Comput..
[19] Nicholas I. M. Gould,et al. On the Evaluation Complexity of Composite Function Minimization with Applications to Nonconvex Nonlinear Programming , 2011, SIAM J. Optim..
[20] Stephen J. Wright,et al. A proximal method for composite minimization , 2008, Mathematical Programming.
[21] Serge Gratton,et al. Recursive Trust-Region Methods for Multiscale Nonlinear Optimization , 2008, SIAM J. Optim..
[22] Stephen P. Boyd,et al. Convex Optimization , 2010, IEEE Transactions on Automatic Control.
[23] Richard G. Carter,et al. Numerical Experience with a Class of Algorithms for Nonlinear Optimization Using Inexact Function and Gradient Information , 1993, SIAM J. Sci. Comput..
[24] Ya-Xiang Yuan,et al. Conditions for convergence of trust region algorithms for nonsmooth optimization , 1985, Math. Program..
[25] D. Gleich. TRUST REGION METHODS , 2017 .
[26] Gitta Kutyniok. Compressed Sensing , 2012 .
[27] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[28] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[29] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[30] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[31] P. Hansen. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .