Special Session 125: Analysis, Algorithms, and Applications of Neural Networks

Entropy-based convergence rates of greedy algorithms

Yuwen Li
Zhejiang University
Peoples Rep of China
Co-Author(s):    Yuwen Li
Abstract:
In this talk, I will present novel convergence estimates of greedy algorithms including the reduced basis method for parametrized PDEs, the empirical interpolation method for approximating parametric functions, and the orthogonal/Chebyshev greedy algorithms for nonlinear dictionary approximation. The proposed convergence rates are all based on the metric entropy of underlying compact sets. This talk is partially based on joint work Jonathan Siegel.