A new descent algorithm for the least absolute value regression problem

1981/01/01 by George O. Wesolowsky · 6 citations
Engineering · Mathematics · Decision Sciences · #Optimization and Mathematical Programming #Advanced Optimization Algorithms Research #Multi-Criteria Decision Making

paper · doi:10.1080/03610918108812224

openalex publication_date 1981/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29

Abstract

This paper presents a new and simple algorithm for the least absolute value regression problem. It is based on the notion of “edge” descent along the surface of the objective function. It is comparable or better in computational efficiency to current linear programming approaches for roughly 4 or fewer independent variables.

Cited by

• Super-resolution Multi-signal Direction-of-Arrival Estimation by Hankel-structured Sensing and Decomposition
• L1 minimization using recursive reduction of dimensionality
• A Maximum Likelihood Approach to Least Absolute Deviation Regression
• The Minimum Sum of Absolute Errors Regression: A State of the Art Survey
• Letters to the Editor
• Least absolute deviations [wikipedia]