Dimensionality Reduction Techniques for Large-Scale Optimization Problems

Coralia Cartis, Oxford University
The tangency of the top surface with the constrained space in the center represents the solution.

Known by many names, sketching techniques allow random projections of data from high to low dimensions while preserving pairwise distances. This talk explores ways to use sketching so as to improve the scalability of algorithms for diverse classes of optimization problems and applications, from linear to nonlinear, local to global, derivative-based to derivative-free. Regression problems and Gauss-Newton techniques will receive particular attention. Numerical illustrations on standard optimization test problems as well as on some machine learning set-ups will be presented.

Please use this link to attend the virtual seminar: