We survey the techniques used for presolving Mixed-integer linear programs (MILPs). Presolving is an important component of all modern MILP solvers. It is used for simplifying a given instance, for detecting any obvious problems or errors, and for identifying structures and characteristics that are useful for solving an instance.

Linear mixed models provide a powerful means of predicting breeding values. However, for many traits of economic importance the assumptions of linear responses, constant variance, and normality are questionable. Generalized linear mixed models provide a means of modeling these deviations from the usual linear mixed model. This paper will examine what constitutes a generalized linear mixed model...

After estimation of e3ects from a linear mixed model, it is often useful to form predicted values for certain factor/variate combinations. This process has been well-de5ned for linear models, but the introduction of random e3ects means that a decision has to be made about the inclusion or exclusion of random model terms from the predictions, including the residual error. For spatially correlate...

Transformation of the response of a linear model is a popular method in practice when attempting to satisfy the assumptions of the model. Environmental research routinely uses log-transformations due to the nature of the observed data. The choice of the transformation is often made based upon previous experience or on the comparison of models with different transformed responses. Often a transf...

Gavril [GA4] defined two new families of intersection graphs: the interval-filament graphs and the subtree-filament graphs. The complements of intervalfilament graphs are the cointerval mixed graphs and the complements of subtree-filament graphs are the cochordal mixed graphs. The family of interval-filament graphs contains the families of cocomparability, polygon-circle, circle and chordal gra...

Mixed linear models can be used to improve the informational value of milk yield forecasts. For this purpose two different functional approaches for modelling lactation curves are as well compared as three linear mixed models with varying random effects of individual animals and lactation numbers. It can be shown that more complex random regression models fit significantly better than fixed reg...