Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
The projected gradient methods for least squares matrix approximations with spectral constraints
SIAM Journal on Numerical Analysis
Balanced realizations via gradient flow techniques
Systems & Control Letters
Geometric optimization methods for adaptive filtering
Geometric optimization methods for adaptive filtering
Numerical Gradient Algorithms for Eigenvalue and Singular Value Calculations
SIAM Journal on Matrix Analysis and Applications
Optimum realizations of sampled-data controllers for FWL sensitivity minimization
Automatica (Journal of IFAC)
The Geometry of Algorithms with Orthogonality Constraints
SIAM Journal on Matrix Analysis and Applications
Numerical Methods for Unconstrained Optimization and Nonlinear Equations (Classics in Applied Mathematics, 16)
Smale's point estimate theory for Newton's method on Lie groups
Journal of Complexity
Statistical Computing on Manifolds: From Riemannian Geometry to Computational Anatomy
Emerging Trends in Visual Computing
Insight into efficient image registration techniques and the demons algorithm
IPMI'07 Proceedings of the 20th international conference on Information processing in medical imaging
Non-parametric diffeomorphic image registration with the demons algorithm
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Optimal estimation and detection in homogeneous spaces
IEEE Transactions on Signal Processing
Diffeomorphic registration of images with variable contrast enhancement
Journal of Biomedical Imaging - Special issue on modern mathematics in biomedical imaging
Editors Choice Article: Visual SLAM: Why filter?
Image and Vision Computing
Gauss---Newton method for convex composite optimizations on Riemannian manifolds
Journal of Global Optimization
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An important class of optimization problems involve minimizing a cost function on a Lie group. In the case where the Lie group is non-compact there is no natural choice of a Riemannian metric and it is not possible to apply recent results on the optimization of functions on Riemannian manifolds. In this paper the invariant structure of a Lie group is exploited to provide a strong interpretation of a Newton iteration on a general Lie group. The paper unifies several previous algorithms proposed in the literature in a single theoretical framework. Local asymptotic quadratic convergence is proved for the algorithms considered.