暂无分享,去创建一个
Dmitriy Drusvyatskiy | Damek Davis | Mateo D'iaz | Damek Davis | D. Drusvyatskiy | M. D'iaz | Mateo Díaz
[1] Nicholas J. A. Harvey,et al. Tight Analyses for Non-Smooth Stochastic Gradient Descent , 2018, COLT.
[2] Claude Lemaréchal,et al. Practical Aspects of the Moreau-Yosida Regularization: Theoretical Preliminaries , 1997, SIAM J. Optim..
[3] Stephen J. Wright,et al. A Line-Search Descent Algorithm for Strict Saddle Functions with Complexity Guarantees , 2020, J. Mach. Learn. Res..
[4] Stephen J. Wright,et al. Trust-Region Newton-CG with Strong Second-Order Complexity Guarantees for Nonconvex Optimization , 2019, SIAM J. Optim..
[5] Yair Carmon,et al. Accelerated Methods for NonConvex Optimization , 2018, SIAM J. Optim..
[6] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[7] Stephen J. Wright,et al. Complexity Analysis of Second-Order Line-Search Algorithms for Smooth Nonconvex Optimization , 2017, SIAM J. Optim..
[8] Bastian Goldlücke,et al. Variational Analysis , 2014, Computer Vision, A Reference Guide.
[9] Dmitriy Drusvyatskiy,et al. Proximal Methods Avoid Active Strict Saddles of Weakly Convex Functions , 2021, Found. Comput. Math..
[10] R. Pemantle,et al. Nonconvergence to Unstable Points in Urn Models and Stochastic Approximations , 1990 .
[11] Furong Huang,et al. Escaping From Saddle Points - Online Stochastic Gradient for Tensor Decomposition , 2015, COLT.
[12] Stephen J. Wright,et al. A log-barrier Newton-CG method for bound constrained optimization with complexity guarantees , 2019, IMA Journal of Numerical Analysis.
[13] Tengyu Ma,et al. Finding approximate local minima faster than gradient descent , 2016, STOC.
[14] Ambuj Tewari,et al. On the Generalization Ability of Online Strongly Convex Programming Algorithms , 2008, NIPS.
[15] Kaizheng Wang,et al. Efficient Clustering for Stretched Mixtures: Landscape and Optimality , 2020, NeurIPS.
[16] John Wright,et al. A Geometric Analysis of Phase Retrieval , 2016, 2016 IEEE International Symposium on Information Theory (ISIT).
[17] Stephen J. Wright,et al. A Newton-CG algorithm with complexity guarantees for smooth unconstrained optimization , 2018, Mathematical Programming.
[18] Maryam Fazel,et al. Escaping from saddle points on Riemannian manifolds , 2019, NeurIPS.
[19] Stephen J. Wright,et al. Complexity of Projected Newton Methods for Bound-constrained Optimization , 2021 .
[20] Yi Zheng,et al. No Spurious Local Minima in Nonconvex Low Rank Problems: A Unified Geometric Analysis , 2017, ICML.
[21] Yurii Nesterov,et al. Cubic regularization of Newton method and its global performance , 2006, Math. Program..
[22] Minhui Huang,et al. Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms , 2021, ArXiv.
[23] Adrian S. Lewis,et al. Convex Analysis And Nonlinear Optimization , 2000 .
[24] Marc Teboulle,et al. Finding Second-Order Stationary Points in Constrained Minimization: A Feasible Direction Approach , 2020, Journal of Optimization Theory and Applications.
[25] Nicholas J. A. Harvey,et al. Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent , 2019, ArXiv.
[26] G. J. O. Jameson,et al. Inequalities for Gamma Function Ratios , 2013, Am. Math. Mon..
[27] Nicolas Boumal,et al. Adaptive regularization with cubics on manifolds , 2018, Mathematical Programming.
[28] Michael I. Jordan,et al. Gradient Descent Only Converges to Minimizers , 2016, COLT.
[29] Tengyu Ma,et al. Matrix Completion has No Spurious Local Minimum , 2016, NIPS.
[30] Nathan Srebro,et al. Global Optimality of Local Search for Low Rank Matrix Recovery , 2016, NIPS.
[31] Michael I. Jordan,et al. On the Local Minima of the Empirical Risk , 2018, NeurIPS.
[32] Dmitriy Drusvyatskiy,et al. Generic Minimizing Behavior in Semialgebraic Optimization , 2015, SIAM J. Optim..
[33] Michael I. Jordan,et al. Gradient Descent Can Take Exponential Time to Escape Saddle Points , 2017, NIPS.
[34] J. Lee,et al. Convergence to Second-Order Stationarity for Constrained Non-Convex Optimization , 2018, 1810.02024.
[35] Michael I. Jordan,et al. First-order methods almost always avoid strict saddle points , 2019, Mathematical Programming.
[36] Dmitriy Drusvyatskiy,et al. Stochastic model-based minimization of weakly convex functions , 2018, SIAM J. Optim..
[37] Songtao Lu,et al. Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems , 2020, NeurIPS.
[38] Michael I. Jordan,et al. On Nonconvex Optimization for Machine Learning , 2019, J. ACM.
[39] Thomas Hofmann,et al. Escaping Saddles with Stochastic Gradients , 2018, ICML.
[40] Nicolas Boumal,et al. Efficiently escaping saddle points on manifolds , 2019, NeurIPS.
[41] Aryan Mokhtari,et al. Escaping Saddle Points in Constrained Optimization , 2018, NeurIPS.
[42] R. Fletcher. A model algorithm for composite nondifferentiable optimization problems , 1982 .
[43] Michael I. Jordan,et al. Accelerated Gradient Descent Escapes Saddle Points Faster than Gradient Descent , 2017, COLT.
[44] Ohad Shamir,et al. Making Gradient Descent Optimal for Strongly Convex Stochastic Optimization , 2011, ICML.
[45] Zhihui Zhu,et al. The Global Optimization Geometry of Low-Rank Matrix Optimization , 2017, IEEE Transactions on Information Theory.
[46] John Wright,et al. When Are Nonconvex Problems Not Scary? , 2015, ArXiv.