We have 2 of 68 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Riemannian gradient descent (RGD) and Riemannian Gauss--Newton (RGN) methods are analyzed, finding that the resulting RGD iterates converge linearly, whereas RGN eventually converges Q-quadratically.
Stochastic gradient methods have gained increasing attention for solving large-scale inverse problems due to their computational efficiency. However, their theoretical justification in the context of ill-posed problems remains underdeveloped, particularly regarding convergence rate analysis, where existing results typi...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
