Prof. Y.-X. Yuan (Chinese Academy of Sciences, China)
Friday, August 2, 2019 - 15:15
Mohrenstr. 39, 10117 Berlin, Erhard-Schmidt-Hörsaal, Erdgeschoss
MATHEON Special Guest Lecture
In this talk, two classes of problems in large scale data analysis and their optimization algorithms will be discussed. The first class focuses on composite convex program problems, where I introduce algorithms including a regularized semi-smooth Newton method, a stochastic semi-smooth Newton method and a parallel subspace correction method. The second class is on optimization with orthogonality constraints, particularly on parallelizable approaches for linear eigenvalue problems and nonlinear eigenvalue problems, and quasi-Newton type methods. Numerical results of applications, e.g., electronic structure calculations, $l_1$-regularized logistic regression problems, Lasso problems and Hartree-Fock total energy minimization problems, will be highlighted.
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