There is a historical gap between UML tools and refactoring tools. The former tools are designed to produce analysis and design models whereas the latter are designed to manipulate program code. MDA tool vendors aim to bridge this gap by regenerating program code from evolving UML OOAD models and vice versa. In this position paper, we describe the problems and a novel solution for implementing ...

We present a new full Nesterov and Todd step infeasible interior-point algorithm for semi-definite optimization. The algorithm decreases the duality gap and the feasibility residuals at the same rate. In the algorithm, we construct strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Every main iteration of the algorithm consists of a feasibility...

We consider a two-stage mixed integer stochastic optimization problem and show that a static robust solution is a good approximation to the fully adaptable two-stage solution for the stochastic problem under fairly general assumptions on the uncertainty set and the probability distribution. In particular, we show that if the right-hand side of the constraints is uncertain and belongs to a symme...

The main focus of this work was the simulation and optimization of a BIPV-façade-system configuration through the development of simulations using the software ”TRNSYS”. First, several limitations and constraints within the TRNSYS framework needed to be solved to develop a reliable simulation environment. Various parameters were detected which are influencing the temperature distribution over a...

This paper studies dynamic stochastic optimization problems parametrized by a random variable. Such problems arise in many applications in operations research and mathematical finance. We give sufficient conditions for the existence of solutions and the absence of a duality gap. Our proof uses extended dynamic programming equations, whose validity is established under new relaxed conditions tha...

The primal-dual Dikin-type affine scaling method was originally proposed for linear optimization and then extended to semidefinite optimization. Here, the method is generalized to symmetric conic optimization using the notion of Euclidean Jordan algebras. The method starts with an interior feasible but not necessarily centered primal-dual solution, and it features both centering and reducing th...

ion gap for i = 1,n a[i] = b[i] * c[i] x[i] = a[i]/y[i] ... ... Figure 1.1. The need to translate from the high-level conceptual view of a digital system, such as a processor capable of executing the program shown above, to its physical realization in VLSI chips leads to the designer’s abstraction gap.

We determine the maximal gap between the optimal values of an integer program and its linear programming relaxation, where the matrix and cost function are fixed but the right hand side is unspecified. Our formula involves irreducible decomposition of monomial ideals. The gap can be computed in polynomial time when the dimension is fixed.