Weak and Strong Superiorization: Between Feasibility-Seeking and Minimization

被引：38

作者：

Censor, Yair ^{[1
]}

机构：

[1] Univ Haifa, Dept Math, IL-3498838 Haifa, Israel

来源：

ANALELE STIINTIFICE ALE UNIVERSITATII OVIDIUS CONSTANTA-SERIA MATEMATICA | 2015年 / 23卷 / 03期

关键词：

perturbation resilience; constrained minimization; convex feasibility problem; dynamic string-averaging; superiorization methodology; superiorized version of an algorithm; strict Fejer monotonicity; PROJECTION METHODS; CONVEX FEASIBILITY; CONVERGENCE; SCHEMES;

D O I：

10.1515/auom-2015-0046

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

We review the superiorization methodology, which can be thought of, in some cases, as lying between feasibility-seeking and constrained minimization. It is not quite trying to solve the full fledged constrained minimization problem; rather, the task is to find a feasible point which is superior (with respect to an objective function value) to one returned by a feasibility-seeking only algorithm. We distinguish between two research directions in the superiorization methodology that nourish from the same general principle: Weak superiorization and strong superiorization and clarify their nature.

引用

页码：41 / 54

页数：14

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