Generalized Graph Signal Sampling by Difference-of-Convex Optimization
The paper addresses sampling beyond bandlimited graph signals by designing an aggregation sampling operator using difference-of-convex (DC) optimization. It reformulates a complex full-rank constraint into a tractable DC optimization problem, solved by the GDPGDC algorithm. This method achieves superior signal recovery compared to existing techniques, especially under mismatched priors.