Excerpt More than 25 years have passed since the first edition of this book was published in
1996. Least squares and least-norm problems have become more significant with every …

A lucid explanation of the intricacies of both simple and complex least squares methods. As
one of the classical statistical regression techniques, and often the first to be taught to new …

The routing of a fleet of vehicles to service a set of customers is important in the field of
distribution. Vehicle routing problems (VRP) arise in many real-life applications within …

A standard errors-in-variables (EIV) model refers to a Gauss–Markov model with an
uncertain model matrix from a geodetic perspective. Least squares within the EIV model is …

It is shown here how–similarly to the unconstrained case–the Constrained Total Least
Squares Estimate (CTLSE) can be generated by solving a certain sequence of eigenvalue …

The multivariate total least-squares (MTLS) approach aims at estimating a matrix of
parameters, Ξ, from a linear model (Y− EY=(X− EX)· Ξ) that includes an observation matrix …

This paper is devoted to the identification of the parameters of discrete fractional systems
with errors in variables. Estimates of the parameters of such systems can be obtained using …

With the introductory paragraph, we explain the fundamental concepts and basic notions of
this section. For you, the analyst, who has the difficult task to deal with measurements …

Proper incorporation of linear and quadratic constraints is critical in estimating parameters
from a system of equations. These constraints may be used to avoid a trivial solution, to …

Error-contaminated systems Ax≈b, for which A is ill-conditioned, are considered. Such
systems may be solved using Tikhonov-like regularized total least squares (RTLS) methods …