Optimization Methods on Riemannian Manifolds and Their Application to Shape Space
By Wolfgang Ring, B. Wirth. SIAM Journal on Optimization, 2012
329 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/29e56fd7c3db3b1de0b66c5338cc173811c3a359
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Nicolas Boumal
- Coralia Cartis
- Pierre-Antoine Absil
- Bamdev Mishra
- Rodolphe Sepulchre
- Toshihiro Iwai
- Hiroyuki Sato
- Luís Nunes Vicente
In Computer Vision and Pattern Recognition
- Rodolphe Sepulchre
- Toshihiro Iwai
- Hiroyuki Sato
- Nicolas Boumal
- Coralia Cartis
- Bamdev Mishra
- Pierre-Antoine Absil
Citations in the corpus
- Global rates of convergence for nonconvex optimization on manifolds (2016, positive): “…descent, nonlinear conjugate gradients, BFGS, Newton’s method and trust-region methods (Nocedal and Wright, 1999; Ruszczy´nski, 2006) have been adapted to apply to the larger framework of (P) (Adler et al., 2002; Absil et al., 2007, 2008; Ring and Wirth, 2012; Huang et al., 2015; Sato, 2016).”
- A new, globally convergent Riemannian conjugate gradient method (2013, methodology): “In [7], the convergence property of Algorithm 3.”
- Manopt, a matlab toolbox for optimization on manifolds (2013, background): “More solvers can be added, with an outlook toward Riemannian BFGS (Ring & Wirth, 2012), stochastic gradients (Bonnabel, 2013), nonsmooth subgradients schemes (Dirr et al. , 2007), etc.”