Generalized left-localized Cayley parametrization for optimization with orthogonality constraints
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Publication:6194124
DOI10.1080/02331934.2022.2142471arXiv2312.01014OpenAlexW4309684182MaRDI QIDQ6194124
Publication date: 19 March 2024
Published in: Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2312.01014
Stiefel manifoldnon-convex optimizationCayley transformorthogonality constraintCayley parametrization
Cites Work
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- A feasible method for optimization with orthogonality constraints
- Accelerated gradient methods for nonconvex nonlinear and stochastic programming
- Optimization algorithms on the Grassmann manifold with application to matrix eigenvalue problems
- A feasible filter method for the nearest low-rank correlation matrix problem
- An augmented Lagrangian approach for sparse principal component analysis
- A framework of constraint preserving update schemes for optimization on Stiefel manifold
- Efficient rank reduction of correlation matrices
- A Riemannian conjugate gradient method for optimization on the Stiefel manifold
- Riemannian conjugate gradient methods with inverse retraction
- Low-rank matrix completion via preconditioned optimization on the Grassmann manifold
- Trust-region methods on Riemannian manifolds
- A constrained optimization algorithm for total energy minimization in electronic structure calculations
- A framework for generalising the Newton method and other iterative methods from Euclidean space to manifolds
- Generalized power method for sparse principal component analysis
- Projection-like Retractions on Matrix Manifolds
- Optimization Methods on Riemannian Manifolds and Their Application to Shape Space
- Manopt, a Matlab toolbox for optimization on manifolds
- A Broyden Class of Quasi-Newton Methods for Riemannian Optimization
- Convergence of Gradient Descent for Low-Rank Matrix Approximation
- Parameter Estimation for Scientists and Engineers
- The Geometry of Algorithms with Orthogonality Constraints
- Steepest Descent Algorithms for Optimization Under Unitary Matrix Constraint
- Trust, But Verify: Fast and Accurate Signal Recovery From 1-Bit Compressive Measurements
- Non-Convex Distributed Optimization
- 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
- Rank reduction of correlation matrices by majorization
- Accelerated Optimization with Orthogonality Constraints
- Riemannian Optimization and Its Applications
- A new, globally convergent Riemannian conjugate gradient method
- A Riemannian Newton Algorithm for Nonlinear Eigenvalue Problems