Convergence and Complexity Guarantee for Inexact First-order Riemannian Optimization Algorithms
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Yuchen, Balzano, Laura, Needell, Deanna, Lyu, Hanbaek |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
by: Li, Yuchen, et al.
Published: (2023)
by: Li, Yuchen, et al.
Published: (2023)
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024)
by: Kwon, Jeongyeol, et al.
Published: (2024)
Block majorization-minimization with diminishing radius for constrained nonsmooth nonconvex optimization
by: Lyu, Hanbaek, et al.
Published: (2020)
by: Lyu, Hanbaek, et al.
Published: (2020)
Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions
by: Diepeveen, Willem, et al.
Published: (2026)
by: Diepeveen, Willem, et al.
Published: (2026)
Stochastic optimization with arbitrary recurrent data sampling
by: Powell, William G., et al.
Published: (2024)
by: Powell, William G., et al.
Published: (2024)
A New First-Order Meta-Learning Algorithm with Convergence Guarantees
by: Chayti, El Mahdi, et al.
Published: (2024)
by: Chayti, El Mahdi, et al.
Published: (2024)
Randomized Kaczmarz Methods with Beyond-Krylov Convergence
by: Dereziński, Michał, et al.
Published: (2025)
by: Dereziński, Michał, et al.
Published: (2025)
Optimal Guarantees for Algorithmic Reproducibility and Gradient Complexity in Convex Optimization
by: Zhang, Liang, et al.
Published: (2023)
by: Zhang, Liang, et al.
Published: (2023)
Subspace Optimization for Large Language Models with Convergence Guarantees
by: He, Yutong, et al.
Published: (2024)
by: He, Yutong, et al.
Published: (2024)
Fine-grained Analysis and Faster Algorithms for Iteratively Solving Linear Systems
by: Dereziński, Michał, et al.
Published: (2024)
by: Dereziński, Michał, et al.
Published: (2024)
Stochastic Compositional Minimax Optimization with Provable Convergence Guarantees
by: Deng, Yuyang, et al.
Published: (2024)
by: Deng, Yuyang, et al.
Published: (2024)
Adam-family Methods for Nonsmooth Optimization with Convergence Guarantees
by: Xiao, Nachuan, et al.
Published: (2023)
by: Xiao, Nachuan, et al.
Published: (2023)
Riemannian Optimization for Non-convex Euclidean Distance Geometry with Global Recovery Guarantees
by: Smith, Chandler, et al.
Published: (2024)
by: Smith, Chandler, et al.
Published: (2024)
Scalable Second-order Riemannian Optimization for $K$-means Clustering
by: Xu, Peng, et al.
Published: (2025)
by: Xu, Peng, et al.
Published: (2025)
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
by: Sattar, Yahya, et al.
Published: (2021)
by: Sattar, Yahya, et al.
Published: (2021)
A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees
by: Zhou, Yuhao, et al.
Published: (2025)
by: Zhou, Yuhao, et al.
Published: (2025)
Memory-Reduced Meta-Learning with Guaranteed Convergence
by: Yang, Honglin, et al.
Published: (2024)
by: Yang, Honglin, et al.
Published: (2024)
MAP Estimation with Denoisers: Convergence Rates and Guarantees
by: Pesme, Scott, et al.
Published: (2025)
by: Pesme, Scott, et al.
Published: (2025)
Convergence Guarantees for RMSProp and Adam in Generalized-smooth Non-convex Optimization with Affine Noise Variance
by: Zhang, Qi, et al.
Published: (2024)
by: Zhang, Qi, et al.
Published: (2024)
From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
by: Gaucher, Renaud, et al.
Published: (2026)
by: Gaucher, Renaud, et al.
Published: (2026)
Convergence and Sample Complexity of First-Order Methods for Agnostic Reinforcement Learning
by: Sherman, Uri, et al.
Published: (2025)
by: Sherman, Uri, et al.
Published: (2025)
Greedy Low-Rank Gradient Compression for Distributed Learning with Convergence Guarantees
by: Chen, Chuyan, et al.
Published: (2025)
by: Chen, Chuyan, et al.
Published: (2025)
Learning Over-Relaxation Policies for ADMM with Convergence Guarantees
by: Lin, Junan, et al.
Published: (2026)
by: Lin, Junan, et al.
Published: (2026)
From PowerSGD to PowerSGD+: Low-Rank Gradient Compression for Distributed Optimization with Convergence Guarantees
by: Xie, Shengping, et al.
Published: (2025)
by: Xie, Shengping, et al.
Published: (2025)
Riemannian Dueling Optimization
by: Ren, Yuxuan, et al.
Published: (2026)
by: Ren, Yuxuan, et al.
Published: (2026)
Modified K-means Algorithm with Local Optimality Guarantees
by: Li, Mingyi, et al.
Published: (2025)
by: Li, Mingyi, et al.
Published: (2025)
Adaptive Algorithms with Sharp Convergence Rates for Stochastic Hierarchical Optimization
by: Gong, Xiaochuan, et al.
Published: (2025)
by: Gong, Xiaochuan, et al.
Published: (2025)
Shuffling Heuristic in Variational Inequalities: Establishing New Convergence Guarantees
by: Medyakov, Daniil, et al.
Published: (2025)
by: Medyakov, Daniil, et al.
Published: (2025)
Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
by: Agafonov, Artem, et al.
Published: (2025)
by: Agafonov, Artem, et al.
Published: (2025)
Inexact Column Generation for Bayesian Network Structure Learning via Difference-of-Submodular Optimization
by: Yang, Yiran, et al.
Published: (2025)
by: Yang, Yiran, et al.
Published: (2025)
Inexact subgradient methods for semialgebraic functions
by: Bolte, Jérôme, et al.
Published: (2024)
by: Bolte, Jérôme, et al.
Published: (2024)
Bilevel Learning with Inexact Stochastic Gradients
by: Salehi, Mohammad Sadegh, et al.
Published: (2024)
by: Salehi, Mohammad Sadegh, et al.
Published: (2024)
Faster Convergence of Riemannian Stochastic Gradient Descent with Increasing Batch Size
by: Oowada, Kanata, et al.
Published: (2025)
by: Oowada, Kanata, et al.
Published: (2025)
GPU-friendly and Linearly Convergent First-order Methods for Certifying Optimal $k$-sparse GLMs
by: Liu, Jiachang, et al.
Published: (2026)
by: Liu, Jiachang, et al.
Published: (2026)
On the Convergence of Adam-Type Algorithm for Bilevel Optimization under Unbounded Smoothness
by: Gong, Xiaochuan, et al.
Published: (2025)
by: Gong, Xiaochuan, et al.
Published: (2025)
Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
by: Oikonomou, Dimitris, et al.
Published: (2024)
by: Oikonomou, Dimitris, et al.
Published: (2024)
QLABGrad: a Hyperparameter-Free and Convergence-Guaranteed Scheme for Deep Learning
by: Fu, Minghan, et al.
Published: (2023)
by: Fu, Minghan, et al.
Published: (2023)
An Inexact Weighted Proximal Trust-Region Method
by: Maia, Leandro Farias, et al.
Published: (2026)
by: Maia, Leandro Farias, et al.
Published: (2026)
Beyond the Ideal: Analyzing the Inexact Muon Update
by: Shulgin, Egor, et al.
Published: (2025)
by: Shulgin, Egor, et al.
Published: (2025)
Fully First-Order Algorithms for Online Bilevel Optimization
by: Jia, Tingkai, et al.
Published: (2026)
by: Jia, Tingkai, et al.
Published: (2026)
Similar Items
-
Convergence and complexity of block majorization-minimization for constrained block-Riemannian optimization
by: Li, Yuchen, et al.
Published: (2023) -
On the Complexity of First-Order Methods in Stochastic Bilevel Optimization
by: Kwon, Jeongyeol, et al.
Published: (2024) -
Block majorization-minimization with diminishing radius for constrained nonsmooth nonconvex optimization
by: Lyu, Hanbaek, et al.
Published: (2020) -
Riemannian Archetypal Analysis: Interpretable non-linear data analysis on deformed star distributions
by: Diepeveen, Willem, et al.
Published: (2026) -
Stochastic optimization with arbitrary recurrent data sampling
by: Powell, William G., et al.
Published: (2024)