Cover Image

An NPDo Approach for Principal Joint SVD-type Block Diagonalization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Li, Ren-Cang, Wang, Li, Yang, Mei
Format:	Preprint
Published:	2026
Subjects:	Numerical Analysis 62H25, 65F30, 65K05, 90C26
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

An NPDo Approach for Principal Joint Block Diagonalization
by: Li, Ren-Cang, et al.
Published: (2026)

An NPDo Approach for Tensor Block-Diagonalization
by: Li, Ren-Cang, et al.
Published: (2026)

Convergence Analysis of Two Alternating Iterative Schemes for Tucker Decomposition
by: Li, Ren-Cang, et al.
Published: (2026)

An NEPv Approach for Feature Selection via Orthogonal OCCA with the (2,1)-norm Regularization
by: Wang, Li, et al.
Published: (2025)

A Theory of the NEPv Approach for Optimization On the Stiefel Manifold
by: Li, Ren-Cang
Published: (2023)

Global optimality conditions for sensor placement, with extensions to binary low-rank A-optimal designs
by: Aarset, Christian
Published: (2024)

Stochastic momentum ADMM for nonconvex and nonsmooth optimization with application to PnP algorithm
by: Deng, Kangkang, et al.
Published: (2025)

FEM-based A-optimal sensor placement for heat source inversion from final time measurement
by: Aarset, Christian, et al.
Published: (2025)

Extrapolated Plug-and-Play Three-Operator Splitting Methods for Nonconvex Optimization with Applications to Image Restoration
by: Wu, Zhongming, et al.
Published: (2024)

The Global R-linear Convergence of Nesterov's Accelerated Gradient Method with Unknown Strongly Convex Parameter
by: Bao, Chenglong, et al.
Published: (2023)

Accelerated Gradient Methods with Gradient Restart: Global Linear Convergence
by: Bao, Chenglong, et al.
Published: (2024)

Rank-one Riemannian Subspace Descent for Nonlinear Matrix Equations
by: Darmwal, Yogesh, et al.
Published: (2026)

A proximal-gradient inertial algorithm with Tikhonov regularization: strong convergence to the minimal norm solution
by: László, Szilárd Csaba
Published: (2024)

A preconditioned difference of convex functions algorithm with extrapolation and line search
by: Zhang, Ran, et al.
Published: (2025)

Spectral conjugate gradient projection methods for large-scale monotone equations without Lipschitz continuity
by: Hamiss, Kabenge, et al.
Published: (2026)

Tight Error Bounds for the Sign-Constrained Stiefel Manifold
by: Chen, Xiaojun, et al.
Published: (2022)

Greedy construction of quadratic manifolds for nonlinear dimensionality reduction and nonlinear model reduction
by: Schwerdtner, Paul, et al.
Published: (2024)

Online learning of quadratic manifolds from streaming data for nonlinear dimensionality reduction and nonlinear model reduction
by: Schwerdtner, Paul, et al.
Published: (2024)

AS-BOX: Additional Sampling Method for Weighted Sum Problems with Box Constraints
by: Krejić, Nataša, et al.
Published: (2025)

Variance-reduced first-order methods for deterministically constrained stochastic nonconvex optimization with strong convergence guarantees
by: Lu, Zhaosong, et al.
Published: (2024)

Functions from $R^2$ to $R^2$: a study in nonlinearity
by: Saldanha, Nicolau C., et al.
Published: (2002)

EnKSGD: A Class Of Preconditioned Black Box Optimization And Inversion Algorithms
by: Irwin, Brian, et al.
Published: (2023)

Efficient Admissible Set Projection in Optimization-based Invariant-Domain-Preserving Limiters for Ideal MHD
by: Liu, Chen, et al.
Published: (2026)

Efficient optimization-based invariant-domain-preserving limiters in solving gas dynamics equations
by: Liu, Chen, et al.
Published: (2025)

Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces
by: Alpay, Natanael, et al.
Published: (2026)

Low-rank optimization on Tucker tensor varieties
by: Gao, Bin, et al.
Published: (2023)

Optimization on product manifolds under a preconditioned metric
by: Gao, Bin, et al.
Published: (2023)

Stable generative modeling using Schrödinger bridges
by: Gottwald, Georg A., et al.
Published: (2024)

Preconditioned Low-Rank Riemannian Optimization for Symmetric Positive Definite Linear Matrix Equations
by: Bioli, Ivan, et al.
Published: (2024)

Goal oriented optimal design of infinite-dimensional Bayesian inverse problems using quadratic approximations
by: Neuberger, J. Nicholas, et al.
Published: (2024)

Taylor Approximation Variance Reduction for Approximation Errors in PDE-constrained Bayesian Inverse Problems
by: Nicholson, Ruanui, et al.
Published: (2025)

A low-rank augmented Lagrangian method for large-scale semidefinite programming based on a hybrid convex-nonconvex approach
by: Monteiro, Renato D. C., et al.
Published: (2024)

ASPEN: An Additional Sampling Penalty Method for Finite-Sum Optimization Problems with Nonlinear Equality Constraints
by: Krejić, Nataša, et al.
Published: (2025)

Large Scale High-Dimensional Reduced-Rank Linear Discriminant Analysis
by: Chi, Jocelyn T.
Published: (2026)

Localized Schrödinger Bridge Sampler
by: Gottwald, Georg A., et al.
Published: (2024)

Model-Driven Subspaces for Large-Scale Optimization with Local Approximation Strategy
by: He, Yitong, et al.
Published: (2025)

A first-order augmented Lagrangian method for constrained minimax optimization
by: Lu, Zhaosong, et al.
Published: (2023)

First-order penalty methods for bilevel optimization
by: Lu, Zhaosong, et al.
Published: (2023)

Solving bilevel optimization via sequential minimax optimization
by: Lu, Zhaosong, et al.
Published: (2025)

A first-order method for nonconvex-strongly-concave constrained minimax optimization
by: Lu, Zhaosong, et al.
Published: (2025)