Parallel non-linear optimization: towards the design of a decision support system for air quality management

• Authors: Andrew Lewis;David Abramson;Rod Simpson
• Venue: SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
• Year: 1997

Large numerical simulation codes have been applied to a wide range of scientific and engineering problems. In the environmental arena the ability to predict the results of certain scenarios by computational science has allowed the choice of strategies which maximize desired outcomes (e.g. financial return) whilst minimizing environmental damage. Access to high performance computing resources has focussed attention on the development of environmental decision support systems which can be used by regulatory agencies and industry planners in evaluating different policy options. A common objective is to find a solution which optimizes some pre-defined criteria. In environmental modelling, the type of optimization problems which need to be considered involve non-linear cost functions over both discrete and continuous parameter values.In this paper we address the optimization component of a decision support system, and perform some initial benchmark studies to assess the effectiveness of the overall approach. The algorithm selected for initial study is based on the quasi-Newton BFGS method. Whilst the BFGS algorithm is generally implemented sequentially, because of the focus of the decision support systems described in the paper we are interested in parallelizing the basic algorithm. This is achieved by concurrent evaluation of functions in finite difference approximations to the derivative and a method of interval subdivision in simple bound constrained line searching.In a realistic problem of air quality management, use of the parallel optimization algorithm as part of an optimizing decision support system is shown to have significant performance gains over other methods of solution. In initial tests it uses less than half the evaluations of a computationally demanding numerical simulation previously used simple enumeration techniques require and is four times faster than traditional sequential optimization methods. This case study has successfully demonstrated the application of an optimization system to a core environmental model, and the feasibility of its use to solve real world problems using parallel and distributed supercomputers.