Cover Image

UAdam: Unified Adam-Type Algorithmic Framework for Non-Convex Stochastic Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Jiang, Yiming, Liu, Jinlan, Xu, Dongpo, Mandic, Danilo P.
Format:	Preprint
Published:	2023
Subjects:	Machine Learning Numerical Analysis Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Convergence of Adam for Non-convex Objectives: Relaxed Hyperparameters and Non-ergodic Case
by: He, Meixuan, et al.
Published: (2023)

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

Towards Quantifying the Preconditioning Effect of Adam
by: Das, Rudrajit, et al.
Published: (2024)

An Efficient Framework for Global Non-Convex Polynomial Optimization over the Hypercube
by: Letourneau, Pierre-David, et al.
Published: (2023)

An Efficient Framework for Global Non-Convex Polynomial Optimization with Algebraic Constraints
by: Harris, Mitchell Tong, et al.
Published: (2023)

Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations
by: Heredia, Carlos
Published: (2024)

TRAFS: A Nonsmooth Convex Optimization Algorithm with $\mathcal{O}\left(\frac{1}ε\right)$ Iteration Complexity
by: Jia, Kai, et al.
Published: (2023)

Tight Convergence Rates in Gradient Mapping for the Difference-of-Convex Algorithm
by: Rotaru, Teodor, et al.
Published: (2025)

PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)

Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)

Active-Set Identification in Noisy and Stochastic Optimization
by: Curtis, Frank E., et al.
Published: (2025)

From Adam to Adam-Like Lagrangians: Second-Order Nonlocal Dynamics
by: Heredia, Carlos
Published: (2026)

Enhanced Adaptive Gradient Algorithms for Nonconvex-PL Minimax Optimization
by: Huang, Feihu, et al.
Published: (2023)

OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)

A Unified Phase-Field Fourier Neural Network Framework for Topology Optimization
by: Li, Jing, et al.
Published: (2025)

Formalization of Complexity Analysis of the First-order Algorithms for Convex Optimization
by: Li, Chenyi, et al.
Published: (2024)

Worth Their Weight: Randomized and Regularized Block Kaczmarz Algorithms without Preprocessing
by: Goldshlager, Gil, et al.
Published: (2025)

Averaged Adam accelerates stochastic optimization in the training of deep neural network approximations for partial differential equation and optimal control problems
by: Dereich, Steffen, et al.
Published: (2025)

Horospherically Convex Optimization on Hadamard Manifolds Part I: Analysis and Algorithms
by: Criscitiello, Christopher, et al.
Published: (2025)

Gradient is All You Need? How Consensus-Based Optimization can be Interpreted as a Stochastic Relaxation of Gradient Descent
by: Riedl, Konstantin, et al.
Published: (2023)

Trust-Region Sequential Quadratic Programming for Stochastic Optimization with Random Models
by: Fang, Yuchen, et al.
Published: (2024)

A New Fast Adaptive Linearized Alternating Direction Multiplier Method for Convex Optimization
by: Wang, Boran
Published: (2024)

Stochastic Langevin Differential Inclusions with Applications to Machine Learning
by: Difonzo, Fabio V., et al.
Published: (2022)

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces
by: Chada, Neil K., et al.
Published: (2023)

Homotopy Methods for Convex Optimization
by: Klingler, Andreas, et al.
Published: (2024)

Error Analysis of Sampling Algorithms for Approximating Stochastic Optimal Control
by: Joshi, Anant A., et al.
Published: (2025)

Cayley Commutator-free Methods for Krotov-Type Algorithms in Quantum Optimal Control
by: Wembe, Boris, et al.
Published: (2026)

Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
by: Karris, Nicholas, et al.
Published: (2024)

Accuracy of Discretely Sampled Stochastic Policies in Continuous-time Reinforcement Learning
by: Jia, Yanwei, et al.
Published: (2025)

Convergence Analysis for A Stochastic Maximum Principle Based Data Driven Feedback Control Algorithm
by: Liang, Siming, et al.
Published: (2024)

A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
by: Fang, Yuchen, et al.
Published: (2026)

Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)

Stochastic Multigrid Method for Blind Ptychographic Phase Retrieval
by: Zhang, Borong, et al.
Published: (2025)

Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
by: Fu, Mingwei, et al.
Published: (2024)

On the Convergence of the Gradient Descent Method with Stochastic Fixed-point Rounding Errors under the Polyak-Lojasiewicz Inequality
by: Xia, Lu, et al.
Published: (2023)

Stochastic Regret Guarantees for Online Zeroth- and First-Order Bilevel Optimization
by: Nazari, Parvin, et al.
Published: (2025)

Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)

Complexity of Zeroth- and First-order Stochastic Trust-Region Algorithms
by: Ha, Yunsoo, et al.
Published: (2024)

A Family of Controllable Momentum Coefficients for Forward-Backward Accelerated Algorithms
by: Fu, Mingwei, et al.
Published: (2025)

Gradient Descent as a Perceptron Algorithm: Understanding Dynamics and Implicit Acceleration
by: Tyurin, Alexander
Published: (2025)