AN EFFICIENT METHOD FOR SOLVING A CLASS OF MATRIX TRACE FUNCTION MINIMIZATION PROBLEM IN MULTIVARIATE STATISTICAL
From MaRDI portal
Publication:5051234
DOI10.12286/jssx.j2019-0583OpenAlexW3188242060MaRDI QIDQ5051234
No author found.
Publication date: 22 November 2022
Full work available at URL: http://www.computmath.com/jssx/EN/10.12286/jssx.j2019-0583
Cites Work
- Unnamed Item
- A feasible method for optimization with orthogonality constraints
- A splitting method for orthogonality constrained problems
- A nonmonotone conjugate gradient algorithm for unconstrained optimization
- Majorization as a tool for optimizing a class of matrix functions
- Minimization of a class of matrix trace functions by means of refined majorization
- Setting up alternating least squares and iterative majorization algorithms for solving various matrix optimization problems
- A Riemannian conjugate gradient method for optimization on the Stiefel manifold
- Numerical study of learning algorithms on Stiefel manifold
- Global convergence of a modified Fletcher-Reeves conjugate gradient method with Armijo-type line search
- A Riemannian Fletcher--Reeves Conjugate Gradient Method for Doubly Stochastic Inverse Eigenvalue Problems
- Low-Rank Matrix Completion by Riemannian Optimization
- A Riemannian variant of the Fletcher-Reeves conjugate gradient method for stochastic inverse eigenvalue problems with partial eigendata
- Adaptive Quadratically Regularized Newton Method for Riemannian Optimization
- An Augmented Lagrangian Method for $\ell_{1}$-Regularized Optimization Problems with Orthogonality Constraints
- A descent modified Polak–Ribière–Polyak conjugate gradient method and its global convergence
- The Geometry of Algorithms with Orthogonality Constraints
- Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint
- Riemannian Newton-type methods for joint diagonalization on the Stiefel manifold with application to independent component analysis
- A New First-Order Algorithmic Framework for Optimization Problems with Orthogonality Constraints
- A non-monotone linear search algorithm with mixed direction on Stiefel manifold
- A Nonmonotone Line Search Technique and Its Application to Unconstrained Optimization
- A Riemannian Optimization Approach to the Matrix Singular Value Decomposition
- Optimization algorithms exploiting unitary constraints
- A Geometric Nonlinear Conjugate Gradient Method for Stochastic Inverse Eigenvalue Problems
This page was built for publication: AN EFFICIENT METHOD FOR SOLVING A CLASS OF MATRIX TRACE FUNCTION MINIMIZATION PROBLEM IN MULTIVARIATE STATISTICAL
