Augmented Lagrangian methods under the constant positive linear dependence constraint qualification

Two Augmented Lagrangian algorithms for solving KKT systems are introduced. The algorithms differ in the way in which penalty parameters are updated. Possibly infeasible accumulation points are characterized. It is proved that feasible limit points that satisfy the Constant Positive Linear Dependence constraint qualification are KKT solutions. Boundedness of the penalty parameters is proved under suitable assumptions. Numerical experiments are presented.

Effects of beacon administration on energy expenditure and substrate utilisation in Psammomys obesus (Israeli sand rats)

Objective: To investigate whether beacon administration affects substrate utilisation, physical activity levels or energy expenditure in Psammomys obesus. Design: Pairs of age- and sex-matched Psammomys obesus were randomly assigned to either beacon-treated (15 µg/day for 7 days (i.c.v.)) or control (i.c.v. saline) groups. Measurements: Indirect calorimetry on day 0 and day 7 to measure oxygen consumption and carbon dioxide production, which were used to calculate fat oxidation, carbohydrate oxidation and total energy expenditure. Physical activity in the calorimeter was measured using an infrared beam system. Food intake and body weight were measured daily. Results: The administration of beacon significantly increased body weight compared to saline-treated control animals. This body weight gain was primarily due to increased body fat content. Average daily food intake tended to be higher in beacon-treated Psammomys obesus, but no effect of beacon administration on substrate oxidation, activity or energy expenditure was detected. Conclusion: The effects of beacon on body weight are due to increased food intake, with no detectable effect on nutrient partitioning, physical activity or energy expenditure.

Compositional Model Repositories via Dynamic Constraint Satisfaction with Order-of-Magnitude Preferences

The predominant knowledge-based approach to automated model construction, compositional modelling, employs a set of models of particular functional components. Its inference mechanism takes a scenario describing the constituent interacting components of a system and translates it into a useful mathematical model. This paper presents a novel compositional modelling approach aimed at building model repositories. It furthers the field in two respects. Firstly, it expands the application domain of compositional modelling to systems that can not be easily described in terms of interacting functional components, such as ecological systems. Secondly, it enables the incorporation of user preferences into the model selection process. These features are achieved by casting the compositional modelling problem as an activity-based dynamic preference constraint satisfaction problem, where the dynamic constraints describe the restrictions imposed over the composition of partial models and the preferences correspond to those of the user of the automated modeller. In addition, the preference levels are represented through the use of symbolic values that differ in orders of magnitude.