Article ID: | iaor19932026 |
Country: | United Kingdom |
Volume: | 20 |
Issue: | 1 |
Start Page Number: | 83 |
End Page Number: | 93 |
Publication Date: | Jan 1993 |
Journal: | Computers and Operations Research |
Authors: | Sklar Michael G., Armstrong Ronald D. |
Keywords: | programming: linear |
With the proliferation of personal computers and the increased interest in robust estimation, a capability of efficiently solving large-scale least absolute values (LAV) problems on a microcomputer would be useful. Least absolute value estimation has gained wide acceptance as a robust alternative to least squares. This paper presents an algorithm for least absolute value estimation which utilizes a Lagrangian decomposition, so that only a small percentage of the linear programming constraints need to be considered during an iteration. One advantage of this method is that it provides the capability of solving large-scale LAV problems on a system where memory requirements are a consideration.