Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Solving a convex set theoretic image recovery problem amounts to finding a point in the intersection of closed and convex sets in a Hilbert space. Methods employing projections onto the individual sets to build a sequence converging to a point in their intersection have proven most useful to obtain set theoretic solutions. They are nonetheless sometimes difficult to implement because of the theoretical or numerical tedium associated with the computation of projections at each iteration.  propose a general parallel iterative method which processes chaotically approximate projections instead of exact ones. ak and strong convergence results are presented and subgradient projection methods are discussed as a particular case. >
['Patrick L. Combettes']
I am writing a research paper on a idea Convex set theoretic image recovery via chaotic iterations of approximate projections
[{"from": "human", "value": "I am writing a research paper on a idea Convex set theoretic image recovery via chaotic iterations of approximate projections"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Solving a convex set theoretic image recovery problem amounts to finding a point in the intersection of closed and convex sets in a Hilbert space. Methods employing projections onto the individual sets to build a sequence converging to a point in their intersection have proven most useful to obtain set theoretic solutions. They are nonetheless sometimes difficult to implement because of the theoretical or numerical tedium associated with the computation of projections at each iteration.  propose a general parallel iterative method which processes chaotically approximate projections instead of exact ones. ak and strong convergence results are presented and subgradient projection methods are discussed as a particular case. >"}]