Convergence analysis of a class of adaptive weighted norm extrapolation algorithms

Adaptive weighted norm extrapolation algorithms can provide superior performance for estimation of sparse signals from limited data. We present theoretical analysis results for a class of these algorithms that include a proof of the global convergence, the rate of convergence derivation, and characterization of the fixed points. We also propose a general class of adaptive weighted extrapolation algorithms and introduce a more general problem formulation which greatly expands the range of applications of the algorithm.<>