Full Text: PDF
Volume 3, Issue 1, 30 April 2021, Pages 21-41
Abstract. The data-compatibility approach to constrained optimization, proposed here, strives to a point that is “close enough” to the solution set and whose target function value is “close enough” to the con- strained minimum value. These notions can replace analysis of asymptotic convergence to a solution point of infinite sequences generated by specific algorithms. We consider a problem of minimizing a convex function over the intersection of the fixed point sets of nonexpansive mappings and demostrate the data-compatibility of the Hybrid Subgradient Method (HSM). A string-averaging HSM is obtained as a by-product and relevance to the minimization over disjoint hard and soft constraints sets is discussed.
How to Cite this Article:
Yair Censor, Maroun Zaknoon, Alexander J. Zaslavski, Data-compatibility of algorithms for constrained convex optimization, J. Appl. Numer. Optim. 3 (2021), 21-41.