Signal processing in dual domain by adaptive projected subgradient method

The goal of this paper is to establish a novel signal processing paradigm that enables us to find a point meeting time-variable specifications in dual domain (e.g., time and frequency domains) simultaneously. For this purpose, we define a new problem which we call adaptive split feasibility problem (ASFP). In the ASFP formulation, we have (i) a priori knowledge based convex constraints in the Euclidean spaces ℝN and ℝM and (ii) data-dependent convex sets in ℝN and ℝM; the latter are obtained in a sequential fashion. Roughly speaking, the problem is to find a common point of all the sets defined on ℝN such that its image under a given linear transformation is a common point of all the sets defined on ℝM, if such a point exists. We prove that the adaptive projected subgradient method (APSM) deals with the ASFP by employing (i) a projected gradient operator with respect to (w.r.t.) a ‘fixed’ proximity function reflecting the convex constraints and (ii) a subgradient projection w.r.t. ‘time-varying’ objective functions reflecting the data-dependent sets. The resulting algorithm requires no unwanted operations such as matrix inversion, therefore it is suitable for real-time implementation. A convergence analysis is presented and verified by numerical examples.