Inverse optimization is the problem of determining the values of missing input parameters that are closest to given estimates and that will make a given target solution optimal. This study is concerned with inverse mixed integer linear optimization problems (MILPs) in which the missing parameters are objective function coefficients. This class generalizes the class studied by Ahuja and Orlin [2...

In a recent paper, this author studied invariant ideals in abelian group algebras under the action of certain infinite, locally finite, quasi-simple groups. While the main result was reasonably definitive, there are nevertheless certain natural extensions that should be considered. One approach to a proof of such extensions is to improve the basic tools that were used in the original work. Howe...

Optimal control problems governed by partial differential equations have become a very active and successful research area. So, in this paper, we analyzed priori posteriori error estimates for the hp finite element discretization of elliptic Robin boundary problems. With discrete continuous optimality conditions problem, constructed estimators. Based on residual model coupled state approximatio...

Polynomial chaos expansions are used to reduce the computational cost in the Bayesian solutions of inverse problems by creating a surrogate posterior that can be evaluated inexpensively. We show, by analysis and example, that when the data contain significant information beyond what is assumed in the prior, the surrogate posterior can be very different from the posterior, and the resulting esti...