The lack of “closed form” solutions for the general linear models resulting from minimising the L0, L1 and L∞ norms is remarked on. Linear and integer programming models for deriving the parameters are given. General solutions are derived for these models by means of Fourier-Motzkin elimination. This results in formulae which can be applied to any data set to obtain the values of the norms and resulting parameters. These formulae also demonstrate known structural results concerning these models as well as providing results for statistical analysis
AbstractIn an earlier report the concept of a mixed integer minimization model (MIMM) was defined an...
Different norms are considered to replace the Euclidean norm in an algorithm given by Fan and Tits (...
The paper discusses how the used norm and corresponding Lipschitz constant influence the speed of al...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
Consider an over-determined linear system A'x = b and an under-determined linear system By = c. Give...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
AbstractIn their article, Abel and Waugh [1] have mentioned the problem of minimizing norms of matri...
A fundamental problem in data analysis is that of fitting a given model to observed data. It is comm...
In this paper, we will derive the condensed Cramer’s rule of Werner for minimal-norm least-squares s...
In many optimization problems, a solution can be viewed as ascribing a “cost” to each client and the...
In this paper, we address the accuracy of the results for the overdetermined full rank linear least ...
We consider the problem of estimating the min-norm solution to a low-rank, linear statistical model....
AbstractIn this paper, we will derive the condensed Cramer’s rule of Werner for minimal-norm least-s...
AbstractThis paper develops a validated numerical algorithm to compute the L∞-norm, a norm which pla...
The need for eliminating redundancies in systems of linear inequalities arises in many applications....
AbstractIn an earlier report the concept of a mixed integer minimization model (MIMM) was defined an...
Different norms are considered to replace the Euclidean norm in an algorithm given by Fan and Tits (...
The paper discusses how the used norm and corresponding Lipschitz constant influence the speed of al...
In this paper, we propose four algorithms for L1 norm computation of regression parameters, where tw...
Consider an over-determined linear system A'x = b and an under-determined linear system By = c. Give...
In this paper, three algorithms for weighted median, simple linear, and multiple m parameters L1 nor...
AbstractIn their article, Abel and Waugh [1] have mentioned the problem of minimizing norms of matri...
A fundamental problem in data analysis is that of fitting a given model to observed data. It is comm...
In this paper, we will derive the condensed Cramer’s rule of Werner for minimal-norm least-squares s...
In many optimization problems, a solution can be viewed as ascribing a “cost” to each client and the...
In this paper, we address the accuracy of the results for the overdetermined full rank linear least ...
We consider the problem of estimating the min-norm solution to a low-rank, linear statistical model....
AbstractIn this paper, we will derive the condensed Cramer’s rule of Werner for minimal-norm least-s...
AbstractThis paper develops a validated numerical algorithm to compute the L∞-norm, a norm which pla...
The need for eliminating redundancies in systems of linear inequalities arises in many applications....
AbstractIn an earlier report the concept of a mixed integer minimization model (MIMM) was defined an...
Different norms are considered to replace the Euclidean norm in an algorithm given by Fan and Tits (...
The paper discusses how the used norm and corresponding Lipschitz constant influence the speed of al...