For solving large-scale optimization programs, an SQP-based interior-point method is to be developed. Hessian updates are performed by the limited memory BFGS method. Very large systems of linear equations are solved by direct and indirect solvers with external data organization. The code is to be integrated into the FE-system Lagrange developed by EADS. The goal is to solve very large mechanical structural design optimization problems.
 

Goal of the project is the development of algorithms and a program library  (toolbox) for solving mixed-integer nonlinear optimization problems. The toolbox is to be integrated into existing simulation software of the SHELL and to be tested on practically relevant examples. 
An SQP algorithm is developed based on trust regions with second-order corrections. Convergence properties of the SQP algorithm are to be analyzed, especially the approximation of derivatives subject to integer variables at grid points. To avoid excessive calculation times for solving mixed-integer quadratic programming subproblems, more efficient methods are to be investigated and implemented, for example by applying early branching by exploiting dual information, or cutting plane techniques known from linear programming (branch-and-cut).
The toolbox is to be tested on academic examples and to be integrated into available simulation software of the SHELL at his institution. Examples are Shell’s Modular Reservoir Simulator (MoReS), Hydrocarbon Field Planning Tool (HFPT), Production Universe (PU), Production and Revenue Optimizer (PRO), and further production control software.
 

Computer-aided design optimization of electronic components is a powerful tool to reduce development costs on the one hand and to improve the performance of bandpass filters on the other. The physical model is based on the wave equations and the piezo-acoustic effect. A mathematical model of an electronic filter depends on certain geometry parameters such as length, height, number of metallized layers, etc., i.e., on continuous variables as well as on integer variables. Proceeding from the design goals of a customer, these geometric parameters of a filter are computed by maximizing the transmission within a given interior frequency range under additional lower bounds for frequencies in certain outer frequency ranges.
However, the mathematical model depends in addition on a couple of integer variables, for example the number of fingers, leading to a more complex mixed integer nonlinear programming problem. One evaluation of the simulation code is extremely time-consuming and derivatives must be approximated by forward differences. Thus, the sequential quadratic programming (SQP) code NLPQL is extended to handle additional integer variables.