Przemysław Wojtaszczyk

Convergence Rates for Greedy Algorithms in Reduced Basis Methods

The reduced basis method was introduced for the accurate online evaluation of solutions to a parameter dependent family of elliptic PDEs. Abstractly, it can be viewed as determining a “good” n-dimensional space

Stability and Instance Optimality for Gaussian Measurements in Compressed Sensing

\mathcal{H}_n

SOME REMARKS ON THE DAUGAVET EQUATION

to be used in approximating the elements of a compact set

Uniqueness of unconditional bases in quasi-banach spaces with applications to hardy spaces, II

\mathcal{F}

Wavelets as unconditional bases inL p (ℝ)

in a Hilbert space

Greedy Wavelet Projections are Bounded on BV

\mathcal{H}

On unconditional polynomial bases inL p and bergman spaces

. One by now popular computational approach is to find

On the structure of Lipschitz-free spaces

\mathcal{H}_n

Orthonormal polynomial bases in function spaces

through a greedy strategy. It is natural to compare the approximation performance of the

Data Assimilation in Reduced Modeling

\mathcal{H}_n