ADMM for Structured Fractional Minimization
Fuente:
arXiv
Saved in:
| Main Author: | Yuan, Ganzhao |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023)
by: Yuan, Ganzhao
Published: (2023)
Adaptive Extrapolated Proximal Gradient Methods with Variance Reduction for Composite Nonconvex Finite-Sum Minimization
by: Yuan, Ganzhao
Published: (2025)
by: Yuan, Ganzhao
Published: (2025)
ADMM for Nonconvex Optimization under Minimal Continuity Assumption
by: Yuan, Ganzhao
Published: (2024)
by: Yuan, Ganzhao
Published: (2024)
KANtrol: A Physics-Informed Kolmogorov-Arnold Network Framework for Solving Multi-Dimensional and Fractional Optimal Control Problems
by: Aghaei, Alireza Afzal
Published: (2024)
by: Aghaei, Alireza Afzal
Published: (2024)
Convergent Proximal Multiblock ADMM for Nonconvex Dynamics-Constrained Optimization
by: Li, Bowen, et al.
Published: (2025)
by: Li, Bowen, et al.
Published: (2025)
Linear convergence of forward-backward accelerated algorithms without knowledge of the modulus of strong convexity
by: Li, Bowen, et al.
Published: (2023)
by: Li, Bowen, et al.
Published: (2023)
Scalable Acceleration for Classification-Based Derivative-Free Optimization
by: Han, Tianyi, et al.
Published: (2023)
by: Han, Tianyi, et al.
Published: (2023)
Nonlinear Assimilation via Score-based Sequential Langevin Sampling
by: Ding, Zhao, et al.
Published: (2024)
by: Ding, Zhao, et al.
Published: (2024)
Block Majorization Minimization with Extrapolation and Application to $β$-NMF
by: Hien, Le Thi Khanh, et al.
Published: (2024)
by: Hien, Le Thi Khanh, et al.
Published: (2024)
Understanding the ADMM Algorithm via High-Resolution Differential Equations
by: Li, Bowen, et al.
Published: (2024)
by: Li, Bowen, et al.
Published: (2024)
ADMM for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2024)
by: Yuan, Ganzhao
Published: (2024)
Convergence Analysis of Fractional Gradient Descent
by: Aggarwal, Ashwani
Published: (2023)
by: Aggarwal, Ashwani
Published: (2023)
Learning rate adaptive stochastic gradient descent optimization methods: numerical simulations for deep learning methods for partial differential equations and convergence analyses
by: Dereich, Steffen, et al.
Published: (2024)
by: Dereich, Steffen, et al.
Published: (2024)
On the numerical reliability of nonsmooth autodiff: a MaxPool case study
by: Boustany, Ryan
Published: (2024)
by: Boustany, Ryan
Published: (2024)
A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks
by: Mishra, Neel, et al.
Published: (2024)
by: Mishra, Neel, et al.
Published: (2024)
Subhomogeneous Deep Equilibrium Models
by: Sittoni, Pietro, et al.
Published: (2024)
by: Sittoni, Pietro, et al.
Published: (2024)
Flattened one-bit stochastic gradient descent: compressed distributed optimization with controlled variance
by: Stollenwerk, Alexander, et al.
Published: (2024)
by: Stollenwerk, Alexander, et al.
Published: (2024)
Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging
by: Banerjee, Amartya, et al.
Published: (2024)
by: Banerjee, Amartya, et al.
Published: (2024)
Anderson Acceleration in Nonsmooth Problems: Local Convergence via Active Manifold Identification
by: Li, Kexin, et al.
Published: (2024)
by: Li, Kexin, et al.
Published: (2024)
Real-time optimal control of high-dimensional parametrized systems by deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)
by: Tomasetto, Matteo, et al.
Published: (2024)
Cubic regularized subspace Newton for non-convex optimization
by: Zhao, Jim, et al.
Published: (2024)
by: Zhao, Jim, et al.
Published: (2024)
Towards Quantifying the Preconditioning Effect of Adam
by: Das, Rudrajit, et al.
Published: (2024)
by: Das, Rudrajit, et al.
Published: (2024)
Super Gradient Descent: Global Optimization requires Global Gradient
by: Achour, Seifeddine
Published: (2024)
by: Achour, Seifeddine
Published: (2024)
Latent feedback control of distributed systems in multiple scenarios through deep learning-based reduced order models
by: Tomasetto, Matteo, et al.
Published: (2024)
by: Tomasetto, Matteo, et al.
Published: (2024)
Quantitative Convergences of Lie Group Momentum Optimizers
by: Kong, Lingkai, et al.
Published: (2024)
by: Kong, Lingkai, et al.
Published: (2024)
Learning incomplete factorization preconditioners for GMRES
by: Häusner, Paul, et al.
Published: (2024)
by: Häusner, Paul, et al.
Published: (2024)
A note on continuous-time online learning
by: Ying, Lexing
Published: (2024)
by: Ying, Lexing
Published: (2024)
Symmetry & Critical Points
by: Arjevani, Yossi
Published: (2024)
by: Arjevani, Yossi
Published: (2024)
Efficient Algorithms for Regularized Nonnegative Scale-invariant Low-rank Approximation Models
by: Cohen, Jeremy E., et al.
Published: (2024)
by: Cohen, Jeremy E., et al.
Published: (2024)
Lyapunov Analysis For Monotonically Forward-Backward Accelerated Algorithms
by: Fu, Mingwei, et al.
Published: (2024)
by: Fu, Mingwei, et al.
Published: (2024)
Suboptimality bounds for trace-bounded SDPs enable a faster and scalable low-rank SDP solver SDPLR+
by: Huang, Yufan, et al.
Published: (2024)
by: Huang, Yufan, et al.
Published: (2024)
Using Linearized Optimal Transport to Predict the Evolution of Stochastic Particle Systems
by: Karris, Nicholas, et al.
Published: (2024)
by: Karris, Nicholas, et al.
Published: (2024)
Dimensionality Reduction Techniques for Global Bayesian Optimisation
by: Long, Luo, et al.
Published: (2024)
by: Long, Luo, et al.
Published: (2024)
Fast Unconstrained Optimization via Hessian Averaging and Adaptive Gradient Sampling Methods
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)
by: O'Leary-Roseberry, Thomas, et al.
Published: (2024)
Mathematical Opportunities in Digital Twins (MATH-DT)
by: Antil, Harbir
Published: (2024)
by: Antil, Harbir
Published: (2024)
Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations
by: Heredia, Carlos
Published: (2024)
by: Heredia, Carlos
Published: (2024)
Learning truly monotone operators with applications to nonlinear inverse problems
by: Belkouchi, Younes, et al.
Published: (2024)
by: Belkouchi, Younes, et al.
Published: (2024)
A Natural Primal-Dual Hybrid Gradient Method for Adversarial Neural Network Training on Solving Partial Differential Equations
by: Liu, Shu, et al.
Published: (2024)
by: Liu, Shu, et al.
Published: (2024)
Similar Items
-
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026) -
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026) -
A Block Coordinate Descent Method for Nonsmooth Composite Optimization under Orthogonality Constraints
by: Yuan, Ganzhao
Published: (2023) -
Adaptive Extrapolated Proximal Gradient Methods with Variance Reduction for Composite Nonconvex Finite-Sum Minimization
by: Yuan, Ganzhao
Published: (2025) -
ADMM for Nonconvex Optimization under Minimal Continuity Assumption
by: Yuan, Ganzhao
Published: (2024)