Convergence Conditions for Variational Inequality Algorithms

Please use this identifier to cite or link to this item: http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/5308

Title:	Convergence Conditions for Variational Inequality Algorithms
Issue Date:	9-Oct-2013
Publisher:	Massachusetts Institute of Technology, Operations Research Center
Description:	Within the extensive variational inequality literature, researchers have developed many algorithms. Depending upon the problem setting, these algorithms ensure the convergence of (i) the entire sequence of iterates, (ii) a subsequence of the iterates, or (iii) averages of the iterates. To establish these convergence results, the literature repeatedly invokes several basic convergence theorems. In this paper, we review these theorems and a few convergence results they imply, and introduce a new result, called the orthogonality theorem, for establishing the convergence of several algorithms for solving a certain class of variational inequalities. Several of the convergence results impose a condition of strong-f-monotonicity on the problem function. We also provide a general overview of the properties of strong-f-monotonicity, including some new results (for example, the relationship between strong-f-monotonicity and convexity).
URI:	http://koha.mediu.edu.my:8181/xmlui/handle/1721
Other Identifiers:	http://hdl.handle.net/1721.1/5308
Appears in Collections:	MIT Items

Files in This Item:

There are no files associated with this item.

DSpace JSPUI