Natural Riemannian gradient for learning functional tensor networks
Fuente:
arXiv
Saved in:
| Main Authors: | Klug, Nikolas, Ulbrich, Michael, Uschmajew, André, Willner, Marius |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Accelerating operator Sinkhorn iteration with overrelaxation
by: Soma, Tasuku, et al.
Published: (2024)
by: Soma, Tasuku, et al.
Published: (2024)
Gauss-Southwell type descent methods for low-rank matrix optimization
by: Olikier, Guillaume, et al.
Published: (2023)
by: Olikier, Guillaume, et al.
Published: (2023)
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
by: Häusner, Paul, et al.
Published: (2023)
by: Häusner, Paul, et al.
Published: (2023)
Numerically stable variants of overrelaxation for operator Sinkhorn iteration
by: Eisenmann, Henrik, et al.
Published: (2026)
by: Eisenmann, Henrik, et al.
Published: (2026)
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
by: Zhang, Fangzhao, et al.
Published: (2024)
by: Zhang, Fangzhao, et al.
Published: (2024)
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)
Numerical Methods for Shape Optimal Design of Fluid-Structure Interaction Problems
by: Haubner, Johannes, et al.
Published: (2024)
by: Haubner, Johannes, et al.
Published: (2024)
Non-asymptotic convergence analysis of the stochastic gradient Hamiltonian Monte Carlo algorithm with discontinuous stochastic gradient with applications to training of ReLU neural networks
by: Liang, Luxu, et al.
Published: (2024)
by: Liang, Luxu, et al.
Published: (2024)
Fast and Provable Tensor-Train Format Tensor Completion via Precondtioned Riemannian Gradient Descent
by: Bian, Fengmiao, et al.
Published: (2025)
by: Bian, Fengmiao, et al.
Published: (2025)
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)
Convergence of two-timescale gradient descent ascent dynamics: finite-dimensional and mean-field perspectives
by: An, Jing, et al.
Published: (2025)
by: An, Jing, et al.
Published: (2025)
A Riemannian rank-adaptive method for higher-order tensor completion in the tensor-train format
by: Vermeylen, Charlotte, et al.
Published: (2024)
by: Vermeylen, Charlotte, et al.
Published: (2024)
A note on continuous-time online learning
by: Ying, Lexing
Published: (2024)
by: Ying, Lexing
Published: (2024)
PADAM: Parallel averaged Adam reduces the error for stochastic optimization in scientific machine learning
by: Jentzen, Arnulf, et al.
Published: (2025)
by: Jentzen, Arnulf, et al.
Published: (2025)
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)
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)
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)
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)
by: Dereich, Steffen, et al.
Published: (2025)
Langevin dynamics based algorithm e-TH$\varepsilon$O POULA for stochastic optimization problems with discontinuous stochastic gradient
by: Lim, Dong-Young, et al.
Published: (2022)
by: Lim, Dong-Young, et al.
Published: (2022)
Fundamental Bias in Inverting Random Sampling Matrices with Application to Sub-sampled Newton
by: Niu, Chengmei, et al.
Published: (2025)
by: Niu, Chengmei, et al.
Published: (2025)
Learned iterative networks: An operator learning perspective
by: Hauptmann, Andreas, et al.
Published: (2025)
by: Hauptmann, Andreas, et al.
Published: (2025)
Dynamic Proximal Gradient Algorithms for Schatten-$p$ Quasi-Norm Regularized Problems
by: Shen, Weiping, et al.
Published: (2026)
by: Shen, Weiping, et al.
Published: (2026)
Why is Normalization Preferred? A Worst-Case Complexity Theory for Stochastically Preconditioned SGD under Heavy-Tailed Noise
by: Fang, Yuchen, et al.
Published: (2026)
by: Fang, Yuchen, et al.
Published: (2026)
State-Dependent Lyapunov Method for Rank-1 Matrix Factorization
by: Moon, Jaehong
Published: (2026)
by: Moon, Jaehong
Published: (2026)
Primal-Dual Methods for Nonsmooth Nonconvex Optimization with Orthogonality Constraints
by: Zhu, Linglingzhi, et al.
Published: (2026)
by: Zhu, Linglingzhi, et al.
Published: (2026)
A distributed semismooth Newton based augmented Lagrangian method for distributed optimization
by: Ma, Qihao, et al.
Published: (2026)
by: Ma, Qihao, et al.
Published: (2026)
Solving the Offline and Online Min-Max Problem of Non-smooth Submodular-Concave Functions: A Zeroth-Order Approach
by: Farzin, Amir Ali, et al.
Published: (2026)
by: Farzin, Amir Ali, et al.
Published: (2026)
WinQ: Accelerating Quantization-Aware Training of Language Models Around Saddle Points
by: Li, Dongyue, et al.
Published: (2026)
by: Li, Dongyue, et al.
Published: (2026)
Last-Iterate Convergence of Randomized Kaczmarz and SGD with Greedy Step Size
by: Dereziński, Michał, et al.
Published: (2026)
by: Dereziński, Michał, et al.
Published: (2026)
OptEMA: Adaptive Exponential Moving Average for Stochastic Optimization with Zero-Noise Optimality
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
Nonlinear model reduction for transport-dominated problems
by: Hesthaven, Jan S., et al.
Published: (2026)
by: Hesthaven, Jan S., et al.
Published: (2026)
Kernel-based potential mean-field games with unbiased random Fourier $U$-statistics
by: Nakano, Yumiharu
Published: (2026)
by: Nakano, Yumiharu
Published: (2026)
Shape Derivative-Informed Neural Operators with Application to Risk-Averse Shape Optimization
by: Gong, Xindi, et al.
Published: (2026)
by: Gong, Xindi, et al.
Published: (2026)
Adaptive Lipschitz-Free Conditional Gradient Methods for Stochastic Composite Nonconvex Optimization
by: Yuan, Ganzhao
Published: (2026)
by: Yuan, Ganzhao
Published: (2026)
Continuum-marginal optimal transport: a mesh-free kernel method
by: Nakano, Yumiharu
Published: (2026)
by: Nakano, Yumiharu
Published: (2026)
Beyond Muon: MUD (MomentUm Decorrelation) for Faster Transformer Training
by: Southworth, Ben S., et al.
Published: (2026)
by: Southworth, Ben S., et al.
Published: (2026)
A Trust-Region Interior-Point Stochastic Sequential Quadratic Programming Method
by: Fang, Yuchen, et al.
Published: (2026)
by: Fang, Yuchen, et al.
Published: (2026)
Scalable Acceleration for Classification-Based Derivative-Free Optimization
by: Han, Tianyi, et al.
Published: (2023)
by: Han, Tianyi, et al.
Published: (2023)
On the numerical reliability of nonsmooth autodiff: a MaxPool case study
by: Boustany, Ryan
Published: (2024)
by: Boustany, Ryan
Published: (2024)
Error Feedback Can Accurately Compress Preconditioners
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
by: Modoranu, Ionut-Vlad, et al.
Published: (2023)
Similar Items
-
Accelerating operator Sinkhorn iteration with overrelaxation
by: Soma, Tasuku, et al.
Published: (2024) -
Gauss-Southwell type descent methods for low-rank matrix optimization
by: Olikier, Guillaume, et al.
Published: (2023) -
Neural incomplete factorization: learning preconditioners for the conjugate gradient method
by: Häusner, Paul, et al.
Published: (2023) -
Numerically stable variants of overrelaxation for operator Sinkhorn iteration
by: Eisenmann, Henrik, et al.
Published: (2026) -
Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
by: Zhang, Fangzhao, et al.
Published: (2024)