Lagrangian approach for large-scale least absolute value estimation

DOI10.1016/0305-0548(93)90098-4zbMath0800.90765OpenAlexW2042758712MaRDI QIDQ1201859

Ronald D. Armstrong, Michael G. Sklar

Publication date: 17 January 1993

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0305-0548(93)90098-4

Mathematics Subject Classification ID

Density estimation (62G07) Large-scale problems in mathematical programming (90C06) Applications of mathematical programming (90C90) Linear programming (90C05) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• Unnamed Item
• Linear programming and \(\ell _ 1\) regression: A geometric interpretation
• Comparison of mathematical programming software: A case study using discrete \(L_ 1\) approximation codes
• Optimal Estimation of Executive Compensation by Linear Programming
• On L1 and Chebyshev estimation
• Linear Programming Techniques for Regression Analysis
• Least absolute value and chebychev estimation utilizing least squares results
• A revised simplex algorithm for the absolute deviation curve fitting problem
• The Lagrangian Relaxation Method for Solving Integer Programming Problems
• An Efficient Method for the Discrete Linear L 1 Approximation Problem
• A Comparison of Two Algorithms for Absolute Deviation Curve Fitting
• On least absolute values estimation
• An Improved Algorithm for Discrete $l_1 $ Linear Approximation

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