We propose stabilized interior penalty discontinuous Galerkin methods for the indefinite time–harmonic Maxwell system. The methods are based on a mixed formulation of the boundary value problem chosen to provide control on the divergence of the electric field. We prove optimal error estimates for the methods in the special case of smooth coefficients and perfectly conducting boundary using a du...

The local influence method has proven to be a useful and powerful tool for detecting influential observations on the estimation of model parameters. This method has been widely applied in different studies related to econometric and statistical modelling. We propose a methodology based on the Lagrange multiplier method with a linear penalty function to assess local influence in the possibly het...

Genetic Algorithms (GA) are, conceptually, suitable to optimize the Assembly Sequence Planning (ASP) problem. GA was implemented in this research to optimize the ASP problem because they can easily handle large search spaces, flexibility in defining the constraints and derive them in a fitness function. A penalty function approach has been used to compute the fitness value for assembly sequence...

Using traditional methods, especially that of eigenfunction expansions combined with the penalty method, sharp conditions are given for the existence of solutions to a general weakly elliptic linear N N second order systems subjected to obstacle type constraints. The result applies to systems with deening coeecient matrices A and B having complex eigenvalues as well as to the case of singular s...

Among machine-learning approaches, reinforcement learning (RL) focuses most on goal-directed learning from interaction. Despite important applications, RL is difficult to design to fit real-world problems because, first, interaction requires too many trial-and-error searches and, second, no guidelines exist on how to design reward and penalty signal values. We are interested in approaches treat...