A practical randomized trust-region method to escape saddle points in high dimension
Fuente:
arXiv
Saved in:
| Main Authors: | Dragomir, Radu-Alexandru, Jiang, Xiaowen, Sun, Bonan, Boumal, Nicolas |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
An adaptive framework for first-order gradient methods
by: Hu, Xiaozhe, et al.
Published: (2026)
by: Hu, Xiaozhe, et al.
Published: (2026)
Fast Reflected Forward-Backward algorithm: achieving fast convergence rates for convex optimization with linear cone constraints
by: Bot, Radu Ioan, et al.
Published: (2024)
by: Bot, Radu Ioan, et al.
Published: (2024)
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods
by: Adcock, Ben, et al.
Published: (2023)
by: Adcock, Ben, et al.
Published: (2023)
Optimization in Theory and Practice
by: Wright, Stephen J.
Published: (2025)
by: Wright, Stephen J.
Published: (2025)
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
by: Azizian, Waïss, et al.
Published: (2022)
by: Azizian, Waïss, et al.
Published: (2022)
An inertial iteratively regularized extragradient method for bilevel variational inequality problems
by: Alves, M. Marques, et al.
Published: (2025)
by: Alves, M. Marques, et al.
Published: (2025)
Gradient descent avoids strict saddles with a simple line-search method too
by: Muşat, Andreea-Alexandra, et al.
Published: (2025)
by: Muşat, Andreea-Alexandra, et al.
Published: (2025)
Minimization Over the Nonconvex Sparsity Constraint Using A Hybrid First-order method
by: Yang, Xiangyu, et al.
Published: (2021)
by: Yang, Xiangyu, et al.
Published: (2021)
A Symplectic Discretization Based Proximal Point Algorithm for Convex Minimization
by: Yuan, Ya-xiang, et al.
Published: (2024)
by: Yuan, Ya-xiang, et al.
Published: (2024)
Grassmannian optimization is NP-hard
by: Lai, Zehua, et al.
Published: (2024)
by: Lai, Zehua, et al.
Published: (2024)
Riemannian Adaptive Regularized Newton Methods with Hölder Continuous Hessians
by: Zhang, Chenyu, et al.
Published: (2023)
by: Zhang, Chenyu, et al.
Published: (2023)
A non-autonomous center-stable set theorem for saddle avoidance in optimization
by: Muşat, Andreea-Alexandra, et al.
Published: (2026)
by: Muşat, Andreea-Alexandra, et al.
Published: (2026)
A Proximal-Gradient Method for Solving Regularized Optimization Problems with General Constraints
by: Curtis, Frank E., et al.
Published: (2025)
by: Curtis, Frank E., et al.
Published: (2025)
A Proximal-Gradient Method for Constrained Optimization
by: Dai, Yutong, et al.
Published: (2024)
by: Dai, Yutong, et al.
Published: (2024)
Generalized sparsity-promoting solvers for Bayesian inverse problems: Versatile sparsifying transforms and unknown noise variances
by: Lindbloom, Jonathan, et al.
Published: (2024)
by: Lindbloom, Jonathan, et al.
Published: (2024)
On the Curvature of the Central Path of Linear Programming Theory
by: Dedieu, Jean-Pierre, et al.
Published: (2003)
by: Dedieu, Jean-Pierre, et al.
Published: (2003)
An accelerated randomized Bregman-Kaczmarz method for strongly convex linearly constraint optimization
by: Tondji, Lionel, et al.
Published: (2025)
by: Tondji, Lionel, et al.
Published: (2025)
Log-Averaged Mirror Prox for Fast, Large-Scale Optimal Transport in Linear Space
by: Burns, Matthew X., et al.
Published: (2025)
by: Burns, Matthew X., et al.
Published: (2025)
Riemannian Newton methods for energy minimization problems of Kohn-Sham type
by: Altmann, R., et al.
Published: (2023)
by: Altmann, R., et al.
Published: (2023)
Curvature-Aware Derivative-Free Optimization
by: Kim, Bumsu, et al.
Published: (2021)
by: Kim, Bumsu, et al.
Published: (2021)
Adaptive first-order methods with enhanced worst-case rates
by: Florea, Mihai I.
Published: (2024)
by: Florea, Mihai I.
Published: (2024)
Stiefel optimization is NP-hard
by: Lai, Zehua, et al.
Published: (2025)
by: Lai, Zehua, et al.
Published: (2025)
Recursive Bound-Constrained AdaGrad with Applications to Multilevel and Domain Decomposition Minimization
by: Gratton, Serge, et al.
Published: (2025)
by: Gratton, Serge, et al.
Published: (2025)
Convergence of iterates and improved rates for accelerated augmented Lagrangian methods for linearly constrained convex optimization
by: He, Xin, et al.
Published: (2026)
by: He, Xin, et al.
Published: (2026)
A Spectral Preconditioner for the Conjugate Gradient Method with Iteration Budget
by: Diouane, Youssef, et al.
Published: (2026)
by: Diouane, Youssef, et al.
Published: (2026)
Swarm-based optimization with random descent
by: Tadmor, Eitan, et al.
Published: (2023)
by: Tadmor, Eitan, et al.
Published: (2023)
Consensus-based optimization for closed-box adversarial attacks and a connection to evolution strategies
by: Roith, Tim, et al.
Published: (2025)
by: Roith, Tim, et al.
Published: (2025)
Gradient Methods with Memory for Minimizing Composite Functions
by: Florea, Mihai I.
Published: (2022)
by: Florea, Mihai I.
Published: (2022)
Convergence of Momentum-Based Optimization Algorithms with Time-Varying Parameters
by: Vidyasagar, Mathukumalli
Published: (2025)
by: Vidyasagar, Mathukumalli
Published: (2025)
An extrapolated and provably convergent algorithm for nonlinear matrix decomposition with the ReLU function
by: Gillis, Nicolas, et al.
Published: (2025)
by: Gillis, Nicolas, et al.
Published: (2025)
An accelerated preconditioned proximal gradient algorithm with a generalized Nesterov momentum for PET image reconstruction
by: Lin, Yizun, et al.
Published: (2024)
by: Lin, Yizun, et al.
Published: (2024)
Variable Projected Augmented Lagrangian Methods for Generalized Lasso Problems
by: Aleotti, Stefano, et al.
Published: (2025)
by: Aleotti, Stefano, et al.
Published: (2025)
Extra-Gradient Method with Flexible Anchoring: Strong Convergence and Fast Residual Decay
by: Boţ, Radu Ioan, et al.
Published: (2024)
by: Boţ, Radu Ioan, et al.
Published: (2024)
Accelerating preconditioned ADMM via degenerate proximal point mappings
by: Sun, Defeng, et al.
Published: (2024)
by: Sun, Defeng, et al.
Published: (2024)
Convex quartic problems: homogenized gradient method and preconditioning
by: Dragomir, Radu-Alexandru, et al.
Published: (2023)
by: Dragomir, Radu-Alexandru, et al.
Published: (2023)
Distributed Gradient-Regularized Newton Method: Scheduled Consensus and O(epsilon^{-1}) Global Iteration Complexity
by: Hu, Wei, et al.
Published: (2026)
by: Hu, Wei, et al.
Published: (2026)
The Intrinsic Riemannian Proximal Gradient Method for Convex Optimization
by: Bergmann, Ronny, et al.
Published: (2025)
by: Bergmann, Ronny, et al.
Published: (2025)
A template for gradient norm minimization
by: Florea, Mihai I.
Published: (2024)
by: Florea, Mihai I.
Published: (2024)
An optimal lower bound for smooth convex functions
by: Florea, Mihai I., et al.
Published: (2024)
by: Florea, Mihai I., et al.
Published: (2024)
Reinterpreting EMML as Mirror Descent for Constrained Maximum Likelihood Estimation
by: Clerc, Antonin, et al.
Published: (2026)
by: Clerc, Antonin, et al.
Published: (2026)
Similar Items
-
An adaptive framework for first-order gradient methods
by: Hu, Xiaozhe, et al.
Published: (2026) -
Fast Reflected Forward-Backward algorithm: achieving fast convergence rates for convex optimization with linear cone constraints
by: Bot, Radu Ioan, et al.
Published: (2024) -
Restarts subject to approximate sharpness: A parameter-free and optimal scheme for first-order methods
by: Adcock, Ben, et al.
Published: (2023) -
Optimization in Theory and Practice
by: Wright, Stephen J.
Published: (2025) -
The rate of convergence of Bregman proximal methods: Local geometry vs. regularity vs. sharpness
by: Azizian, Waïss, et al.
Published: (2022)