Abstract
It has been known for many years that a robust solution to an overdetermined system of linear equations Ax ≈ b is obtained by minimizing the L1 norm of the residual error. A correct solution x to the linear system can often be obtained in this way, in spite of large errors (outliers) in some elements of the (m × n) matrix A and the data vector b. This is in contrast to a least squares solution, where even one large error will typically cause a large error in x. In this paper we give necessary and sufficient conditions that the correct solution is obtained when there are some errors in A and b. Based on the sufficient condition, it is shown that if k rows of [A b] contain large errors, the correct solution is guaranteed if (m − n)/n ≥ 2k/σ, where σ > 0, is a lower bound of singular values related to A. Since m typically represents the number of measurements, this inequality shows how many data points are needed to guarantee a correct solution in the presence of large errors in some of the data. This inequality is, in fact, an upper bound, and computational results are presented, which show that the correct solution will be obtained, with high probability, for much smaller values of m − n.
Similar content being viewed by others
References
T.J. Abatzoglou, J.M. Mendel, and G.A. Harada, “The constrained total least squares technique and its application to harmonic superresolution,” IEEE Trans. Signal Process., vol. 39, pp. 1070-1087, 1991.
H. Barkhuijsen, R. De Beer, and D. Van Ormondt, “Improved algorithm for noniterative time-domain model fitting to exponentially damped magnetic resonance signals,” J. Magnetic Resonance, vol. 73, pp. 553-557, 1987.
I. Barrodale, “L1 approximation and the analysis of data,” Appl. Stat., vol. 17, pp. 51-57, 1968.
I. Barrodale and F.D.K. Roberts, “An improved algorithm for discrete L 1 linear approximation,” SIAM J. Numer. Anal., vol. 10, pp. 839-848, 1973.
I. Barrodale and A. Young, “Algorithms for best L 1 and L∞ linear approximations on a discrete set,” Numer. Math., vol. 8, pp. 295-306, 1966.
A. Bjorck, Numerical Methods for Least Squares Problems, SIAM: Philadelphia, PA, 1996.
R. De Beer and D. Van Ormondt, “Analysis of NMR data using time-domain fitting procedures,” in In-vivo Magnetic Resonance Spectroscopy I: Probeheads, Radiofrequency Pulses, Spectrum Analysis, NMR Basic Principles and Progress 26, M. Rudin (Ed.), Springer-Verlag: Berlin, 1992, pp. 201-248.
T.E. Dielman, “Least absolute value estimation in regression models: An annotated bibliography,” Commun. Statistical-Theor. Meth., vol. 13, pp. 513-541, 1984.
T.E. Dielman and E.L. Rose, “Forecasting in least absolute value regression with autocorrelated errors: A small sample study,” Int. J. Forecast, vol. 10, pp. 539-547, 1994.
J. Dupacova, “Robustness of L 1 regression in the light of linear programming,” in L 1-statistical Analysis and Related Methods, Y. Dodge (Ed.), North-Holland: Amsterdam, 1992.
R. Kumaresan and D.W. Tufts, “Estimating the parameters of exponentially damped sinusoids and pole-zero modeling in noise,” IEEE Trans. on Acoust., Speech, and Signal Process., vol. 30, pp. 833-840, 1982.
L. Ljung, System Identification Theory for the User, Prentice-Hall: Englewood Cliffs, NJ, 1987.
P.W. Mielke, Jr and K.J. Berry, “Permutation-based multivariate regression analysis: The case for least sum of absolute deviations regression,” Ann. Oper. Res., vol. 74, pp. 259-268, 1997.
S.C. Narula and J.F. Wellington, “The minimum sum of absolute errors regression: A state of the art survey,” Int. Statist. Rev., vol. 50, pp. 317-326, 1982.
M.A. Rahman and K.B. Yu, “Total least squares approach for frequency estimation using linear prediction,” IEEE Trans. Acous. Speech Signal Process., vol. 35, pp. 1440-1454, 1987.
J.R. Rice and J.S. White, “Norms for smoothing and estimation,” SIAM Rev., vol. 6, pp. 243-256, 1964.
J.B. Rosen, H. Park, and J. Glick, “Total least norm formulation and solution for structured problems,” SIAM J. Matrix Anal. Appl., vol. 17, pp. 110-128, 1996.
J.B. Rosen, H. Park, and J. Glick, “Structured total least norm for nonlinear problems,” SIAM J. Matrix Anal. Appl., vol. 20, pp. 14-30, 1999.
J.B. Rosen, H. Park, and J. Glick, “Signal identification using a least L 1 norm algorithm,” Optimization & Engineering, vol. 1, pp. 51-65, 2000.
P.J. Rousseeuw and A.M. Leroy, Robust Regression and Outlier Detection, John Wiley: New York, 1987.
S. Van Huffel, H. Park, and J.B. Rosen, “Formulation and solution of structured total least norm problems for parameter estimation,” IEEE Trans. Signal Process., vol. 44, pp. 2464-2474, 1996.
S. Van Huffel and J. Vandewalle, The Total Least Squares Problem, Computational Aspects and Analysis, SIAM: Philadelphia, PA, 1991.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Rosen, J.B., Park, H., Glick, J. et al. Accurate Solution to Overdetermined Linear Equations with Errors Using L1 Norm Minimization. Computational Optimization and Applications 17, 329–341 (2000). https://doi.org/10.1023/A:1026562601717
Issue Date:
DOI: https://doi.org/10.1023/A:1026562601717