scholarly journals Optimal Solution Properties of an Overdetermined System of Linear Equations

2019 ◽  
Vol 12 (4) ◽  
pp. 1360-1370
Author(s):  
Vedran Novoselac ◽  
Zlatko Pavic

The paper considers the solution properties of an overdetermined system of linear equations in a given norm. The problem is observed as a minimization of the corresponding functional of the errors. Presenting the main results of $p$ norm it is shown that the functional is convex. Following the convex properties we examine minimization properties showing that the problem possesses regression, scale, and affine equivariant properties. As an example we illustrated the problem of finding weighted mean and weighted median of the data.

2019 ◽  
Vol 1 (1) ◽  
pp. 19-30
Author(s):  
Bijan Bidabad

In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 norm regressions are introduced. The corresponding computer programs are also included.   


Author(s):  
Jack-Kang Chan

We show that the well-known least squares (LS) solution of an overdetermined system of linear equations is a convex combination of all the non-trivial solutions weighed by the squares of the corresponding denominator determinants of the Cramer's rule. This Least Squares Decomposition (LSD) gives an alternate statistical interpretation of least squares, as well as another geometric meaning. Furthermore, when the singular values of the matrix of the overdetermined system are not small, the LSD may be able to provide flexible solutions. As an illustration, we apply the LSD to interpret the LS-solution in the problem of source localization.


1957 ◽  
Vol 4 (3) ◽  
pp. 341-347 ◽  
Author(s):  
Allen A. Goldstein ◽  
Norman Levine ◽  
James B. Hereshoff

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