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Alexander J. Zaslavski, Superiorization with a projected subgradient method

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DOI: 10.23952/jano.4.2022.2.11
Volume 4, Issue 2, 1 August 2022, Pages 291-298 

 

Abstract. In this paper, we study a constrained minimization problem with a convex objective function and a feasible region, which is the intersection of finitely many closed convex constraint sets. We use a projected subgradient method combined with a dynamic string-averaging projection method with variable strings and variable weights, as a feasibility-seeking algorithm. It is shown that any sequence, generated by the superiorized version of a dynamic string-averaging projection algorithm, not only converges to a feasible point but, additionally, also either its limit point solves the constrained minimization problem or the sequence is strictly Fejér monotone with respect to the solution set.

 

How to Cite this Article:
A.J. Zaslavski, Superiorization with a projected subgradient method, J. Appl. Numer. Optim. 4 (2022), 291-298.