Cover Image

Convergence Analysis of the Wasserstein Proximal Algorithm beyond Geodesic Convexity

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhu, Shuailong, Chen, Xiaohui
Format:	Preprint
Published:	2025
Subjects:	Optimization and Control Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Escaping Saddle Points for Nonsmooth Weakly Convex Functions via Perturbed Proximal Algorithms
by: Huang, Minhui, et al.
Published: (2021)

Convergence Rate of the Last Iterate of Stochastic Proximal Algorithms
by: Vaidyan, Kevin Kurian Thomas, et al.
Published: (2026)

Convergence Analysis of the PAGE Stochastic Algorithm for Weakly Convex Finite-Sum Optimization
by: Condat, Laurent, et al.
Published: (2025)

Sobolev Gradient Ascent for Optimal Transport: Barycenter Optimization and Convergence Analysis
by: Kim, Kaheon, et al.
Published: (2025)

A Provably Convergent and Practical Algorithm for Gromov--Wasserstein Optimal Transport
by: Liang, Ling, et al.
Published: (2026)

Disciplined Geodesically Convex Programming
by: Cheng, Andrew, et al.
Published: (2024)

Wasserstein Proximal Coordinate Gradient Algorithms
by: Yao, Rentian, et al.
Published: (2024)

The Ball-Proximal (="Broximal") Point Method: a New Algorithm, Convergence Theory, and Applications
by: Gruntkowska, Kaja, et al.
Published: (2025)

Damped Proximal Augmented Lagrangian Method for weakly-Convex Problems with Convex Constraints
by: Dahal, Hari, et al.
Published: (2023)

Wasserstein Distributionally Robust Shallow Convex Neural Networks
by: Pallage, Julien, et al.
Published: (2024)

Compressed Proximal Federated Learning for Non-Convex Composite Optimization on Heterogeneous Data
by: Qiu, Pu, et al.
Published: (2026)

A Short and Unified Convergence Analysis of the SAG, SAGA, and IAG Algorithms
by: Zhu, Feng, et al.
Published: (2026)

Accelerated Gradient Methods for Geodesically Convex Optimization: Tractable Algorithms and Convergence Analysis
by: Kim, Jungbin, et al.
Published: (2022)

Muon Does Not Converge on Convex Lipschitz Functions
by: Parshakova, Tetiana, et al.
Published: (2026)

Sparse Transformer Architectures via Regularized Wasserstein Proximal Operator with $L_1$ Prior
by: Han, Fuqun, et al.
Published: (2025)

A New Convergence Analysis of Plug-and-Play Proximal Gradient Descent Under Prior Mismatch
by: Xu, Guixian, et al.
Published: (2026)

On Linear Convergence in Smooth Convex-Concave Bilinearly-Coupled Saddle-Point Optimization: Lower Bounds and Optimal Algorithms
by: Kovalev, Dmitry, et al.
Published: (2024)

Natural Hypergradient Descent: Algorithm Design, Convergence Analysis, and Parallel Implementation
by: Kong, Deyi, et al.
Published: (2026)

On Convergence of Incremental Gradient for Non-Convex Smooth Functions
by: Koloskova, Anastasia, et al.
Published: (2023)

Convergence of Some Convex Message Passing Algorithms to a Fixed Point
by: Voracek, Vaclav, et al.
Published: (2024)

Spurious Stationarity and Hardness Results for Bregman Proximal-Type Algorithms
by: Chen, He, et al.
Published: (2024)

Adam-SHANG: A Convergent Adam-Type Method for Stochastic Smooth Convex Optimization
by: Yu, Yaxin, et al.
Published: (2026)

Lower Bounds and Proximally Anchored SGD for Non-Convex Minimization Under Unbounded Variance
by: Fazla, Arda, et al.
Published: (2026)

Quantitative Convergence Analysis of Projected Stochastic Gradient Descent for Non-Convex Losses via the Goldstein Subdifferential
by: Zheng, Yuping, et al.
Published: (2025)

Adaptive Proximal Gradient Method for Convex Optimization
by: Malitsky, Yura, et al.
Published: (2023)

Joint Cooperative and Non-Cooperative Localization in WSNs with Distributed Scaled Proximal ADMM Algorithms
by: Zhu, Qiaojia, et al.
Published: (2025)

Functionally Constrained Algorithm Solves Convex Simple Bilevel Problems
by: Zhang, Huaqing, et al.
Published: (2024)

The Stochastic Proximal Distance Algorithm
by: Jiang, Haoyu, et al.
Published: (2022)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)

Enhancing Distributional Robustness in Principal Component Analysis by Wasserstein Distances
by: Wang, Lei, et al.
Published: (2025)

ADMM Algorithms for Residual Network Training: Convergence Analysis and Parallel Implementation
by: Xu, Jintao, et al.
Published: (2023)

Shuffling Momentum Gradient Algorithm for Convex Optimization
by: Tran, Trang H., et al.
Published: (2024)

Proximal Interacting Particle Langevin Algorithms
by: Encinar, Paula Cordero, et al.
Published: (2024)

Improved Last-Iterate Convergence of Shuffling Gradient Methods for Nonsmooth Convex Optimization
by: Liu, Zijian, et al.
Published: (2025)

Bilevel Optimization under Unbounded Smoothness: A New Algorithm and Convergence Analysis
by: Hao, Jie, et al.
Published: (2024)

Optimal Algorithms for Online Convex Optimization with Adversarial Constraints
by: Sinha, Abhishek, et al.
Published: (2023)

Learning Algorithm Hyperparameters for Fast Parametric Convex Optimization
by: Sambharya, Rajiv, et al.
Published: (2024)

An Optimistic Algorithm for Online Convex Optimization with Adversarial Constraints
by: Lekeufack, Jordan, et al.
Published: (2024)

Wasserstein Convergence of Score-based Generative Models under Semiconvexity and Discontinuous Gradients
by: Bruno, Stefano, et al.
Published: (2025)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
by: Sato, Kanji, et al.
Published: (2022)