An algorithm for the minimization of nonsmooth nonconvex functions using inexact evaluations and its worst-case complexity
暂无分享,去创建一个
E. Simon | S. Gratton | Ph. L. Toint | P. Toint | S. Gratton | E. Simon
[1] Vyacheslav Kungurtsev,et al. A Subsampling Line-Search Method with Second-Order Results , 2018, INFORMS J. Optim..
[2] Stefania Bellavia,et al. Deterministic and stochastic inexact regularization algorithms for nonconvex optimization with optimal complexity , 2018, ArXiv.
[3] Stephen P. Boyd,et al. Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.
[4] Nicholas I. M. Gould,et al. Trust Region Methods , 2000, MOS-SIAM Series on Optimization.
[5] Dmitriy Drusvyatskiy,et al. Stochastic model-based minimization of weakly convex functions , 2018, SIAM J. Optim..
[6] David L Donoho,et al. Compressed sensing , 2006, IEEE Transactions on Information Theory.
[7] Serge Gratton,et al. Recursive Trust-Region Methods for Multiscale Nonlinear Optimization , 2008, SIAM J. Optim..
[8] Nicholas I. M. Gould,et al. On the Oracle Complexity of First-Order and Derivative-Free Algorithms for Smooth Nonconvex Minimization , 2012, SIAM J. Optim..
[9] Katya Scheinberg,et al. Convergence Rate Analysis of a Stochastic Trust-Region Method via Supermartingales , 2016, INFORMS Journal on Optimization.
[10] Nicholas I. M. Gould,et al. On the Evaluation Complexity of Composite Function Minimization with Applications to Nonconvex Nonlinear Programming , 2011, SIAM J. Optim..
[11] Warren Hare,et al. A proximal bundle method for nonsmooth nonconvex functions with inexact information , 2015, Computational Optimization and Applications.
[12] D. L. Donoho,et al. Compressed sensing , 2006, IEEE Trans. Inf. Theory.
[13] P. Hansen. Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion , 1987 .
[14] Nicholas I. M. Gould,et al. Sharp worst-case evaluation complexity bounds for arbitrary-order nonconvex optimization with inexpensive constraints , 2018, SIAM J. Optim..
[15] José Mario Martínez,et al. Worst-case evaluation complexity for unconstrained nonlinear optimization using high-order regularized models , 2017, Math. Program..
[16] Tianyi Lin,et al. On Adaptive Cubic Regularized Newton's Methods for Convex Optimization via Random Sampling , 2018 .
[17] Marc Teboulle,et al. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems , 2009, SIAM J. Imaging Sci..
[18] Cho-Jui Hsieh,et al. Stochastic Second-order Methods for Non-convex Optimization with Inexact Hessian and Gradient , 2018, ArXiv.
[19] Stephen J. Wright,et al. A proximal method for composite minimization , 2008, Mathematical Programming.
[20] Katya Scheinberg,et al. Global convergence rate analysis of unconstrained optimization methods based on probabilistic models , 2015, Mathematical Programming.
[21] Feng Ruan,et al. Stochastic Methods for Composite and Weakly Convex Optimization Problems , 2017, SIAM J. Optim..
[22] 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..
[23] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[24] Ya-Xiang Yuan,et al. Conditions for convergence of trust region algorithms for nonsmooth optimization , 1985, Math. Program..
[25] Daniel Brand,et al. Training Deep Neural Networks with 8-bit Floating Point Numbers , 2018, NeurIPS.
[26] Tianyi Lin,et al. Adaptively Accelerating Cubic Regularized Newton's Methods for Convex Optimization via Random Sampling , 2018 .
[27] Serge Gratton,et al. A note on solving nonlinear optimization problems in variable precision , 2018, Computational Optimization and Applications.
[28] Alexander J. Smola,et al. Proximal Stochastic Methods for Nonsmooth Nonconvex Finite-Sum Optimization , 2016, NIPS.
[29] Matthias Heinkenschloss,et al. Inexact Objective Function Evaluations in a Trust-Region Algorithm for PDE-Constrained Optimization under Uncertainty , 2014, SIAM J. Sci. Comput..
[30] S. Bellavia,et al. Adaptive Regularization Algorithms with Inexact Evaluations for Nonconvex Optimization , 2018, SIAM J. Optim..
[31] Peng Xu,et al. Newton-type methods for non-convex optimization under inexact Hessian information , 2017, Math. Program..
[32] Michael W. Mahoney,et al. Sub-sampled Newton methods , 2018, Math. Program..
[33] Yurii Nesterov,et al. Introductory Lectures on Convex Optimization - A Basic Course , 2014, Applied Optimization.
[34] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.