Signal Processing Structures for Solving Conservative Constraint Satisfaction Problems
Provides theoretical guarantees for a class of signal processing structures applied to constraint satisfaction, but the results are incremental and domain-specific.
The paper establishes sufficient conditions for using conservative signal processing structures to solve constraint satisfaction problems, demonstrating convergence and robustness against communication and processing delays. Numerical experiments validate the theoretical results.
This primary purpose of this paper is to succinctly state a number of verifiable and tractable sufficient conditions under which a particular class of conservative signal processing structures may be readily used to solve a companion class of constraint satisfaction problems using both synchronous and asynchronous implementation protocols. In particular, the mentioned class of structures is shown to have desirable convergence and robustness properties with respect to various uncertainties involving communication and processing delays. Essential ingredients to the arguments herein involve blending together functional composition methods, conservation principles, asynchronous signal processing implementation protocols, and methods of homotopy. Numerical experiments complement the theoretical presentation and connections to optimization theory are made.