Linear programming: active set analysis and computer programs
Linear programming: active set analysis and computer programs
on Statistical data analysis based on the L1-norm and related methods
on Statistical data analysis based on the L1-norm and related methods
Journal of Optimization Theory and Applications
ACM Transactions on Mathematical Software (TOMS)
Algorithm 552: Solution of the Constrained I1 Linear Approximation Problem [F4]
ACM Transactions on Mathematical Software (TOMS)
Linearly Constrained Discrete I1 Problems
ACM Transactions on Mathematical Software (TOMS)
Algorithm 563: A Program for Linearly Constrained Discrete I1 Problems
ACM Transactions on Mathematical Software (TOMS)
Finite Algorithms in Optimization and Data Analysis
Finite Algorithms in Optimization and Data Analysis
A New Finite Continuation Algorithm for Linear Programming
SIAM Journal on Optimization
The Linear l1 Estimator and the Huber M-Estimator
SIAM Journal on Optimization
Computational Statistics & Data Analysis
Design of recurrent neural networks for solving constrained least absolute deviation problems
IEEE Transactions on Neural Networks
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An implementation of the reduced gradient algorithm is proposed to solve the linear L1 estimation problem (least absolute deviations regression) with linear equality or inequality constraints, including rank deficient and degenerate cases. Degenerate points are treated by solving a derived L1 problem to give a descent direction. The algorithm is a direct descent, active set method that is shown to be finite, it is geometrically motivated and simpler than the projected gradient algorithm (PGA) of Bartels, Conn and Sinclair, which uses a penalty function approach for the constrained case. Computational experiments indicate that the proposed algorithm compares favourably, both in reliability and efficiency, to the PGA, to the algorithms ACM551 and AFK (which use an LP formulation of the L1 problem) and to LPASL1 (which is based on the Huber approximation method of Madsen, Nielsen and Pinar). Although it is not as efficient as ACM552 (Barrodale-Roberts algorithm) on large scale unconstrained problems, it performs better on large scale problems with bounded variable constraints.