Compressed sensing methods using sparse measure- ment matrices and iterative message-passing recovery procedures are recently investigated due to their low computational complex- ity and excellent performance. The design and analysis of this class of methods is inspired by a large volume of work on sparse- graph codes such as Low-Density Parity-Check (LDPC) codes and the iterative Belief-Propagation (BP) decoding algorithms. In par- ticular, we focus on a class of compressed sensing methods emerg- ing from the Sudocodes scheme that follow similar ideas used in a class of sparse-graph codes called rateless codes. We are inter- ested in the design and analysis of adaptive Sudocodes methods and this paper provides initial steps in this direction.