On the rate of convergence of greedy algorithms

We prove some results on the rate of convergence of greedy algorithms, which provide expansions. We consider both the case of Hilbert spaces and the more general case of Banach spaces. The new ingredient of the paper is that we bound the error of approximation by the product of both norms -- the norm of $f$ and the $A_1$-norm of $f$. Typically, only the $A_1$-norm of $f$ is used. In particular, we establish that some greedy algorithms (Pure Greedy Algorithm (PGA) and its generalizations) are as good as the Orthogonal Greedy Algorithm (OGA) in this new sense of the rate of convergence, while it is known that the PGA is much worth than the OGA in the standard sense

Greedy expansions with prescribed coefficients in Hilbert spaces for special classes of dictionaries

Greedy expansions with prescribed coefficients have been introduced by V. N. Temlyakov in the frame of Banach spaces. The idea is to choose a sequence of fixed (real) coefficients $\{c_n\}_{n=1}^\infty$ and a fixed set of elements (dictionary) of the Banach space; then, under suitable conditions on the coefficients and the dictionary, it is possible to expand all the elements of the Banach space in series that contain only the fixed coefficients and the elements of the dictionary. In Hilbert spaces the convergence of greedy algorithm with prescribed coefficients is characterized, in the sense that there are necessary and sufficient conditions on the coefficients in order that the algorithm is convergent for all the dictionaries. This paper is concerned with the question if such conditions can be weakened for particular dictionaries; we prove that this is the case for some classes of dictionaries related to orthonormal sequences