  The orthogonal matching pursuit (OMP) is one of the mainstream algorithms for
sparse data reconstruction or approximation. It acts as a driving force for the
development of several other greedy methods for sparse data reconstruction, and
it also plays a vital role in the development of compressed sensing theory for
sparse signal and image reconstruction. In this paper, we propose the so-called
dynamic orthogonal matching pursuit (DOMP) and enhanced dynamic orthogonal
matching pursuit (EDOMP) algorithms which are more efficient than OMP for
sparse data reconstruction from a numerical point of view. We carry out a
rigorous analysis to establish the reconstruction error bound for DOMP under
the restricted isometry property of the measurement matrix. The main result
claims that the reconstruction error via DOMP can be controlled and measured in
terms of the number of iterations, sparsity level of data, and the noise level
of measurements. Moveover, the finite convergence of DOMP for a class of
large-scale compressed sensing problems is also shown.
