Wepropose a schemefor producingLatin hypercube samples that can enhanceany of the existing sampling algorithms in Bayesiannetworks. Wetest this scheme in combinationwith the likel...
We propose a sample average approximation (SAA) method for stochastic programming problems involving an expected value constraint. Such problems arise, for example, in portfolio s...
We consider a two-dimensional problem of positron emission tomography where the random mechanism of the generation of the tomographic data is modeled by Poisson processes. The goa...
A common problem in applied mathematics is that of finding a function in a Hilbert space with prescribed best approximations from a finite number of closed vector subspaces. In ...
Interior point methods (IPMs) have proven to be an efficient way of solving quadratic programming problems in predictive control. A linear system of equations needs to be solved in...
Amir Shahzad, Eric C. Kerrigan, George A. Constant...