Space Decomposition Methods in Nonsmooth Optimization

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Robt. Mifflin Abstract for DMS-9703952 Abstract for Space Decomposition Methods in Nonsmooth Optimization The proposed research concerns developing theory and methods for implicitly combining polyhedral and quadratic approximation to produce a rapidly convergent algorithm for minimizing a nonsmooth function of many variables. The idea is to use a bundle method to determine a so-called VU-space decomposition such that on V-space the cutting-plane aspect of bundling works fast and on U-space a quasi-Newton approximation of a Hessian of a U-Lagrangian can be employed. Such methods can be used to solve large or complicated optimization models via separation of variables or constraints. For example, a water pressure control problem of finding values for flow rates and pump pressure gaps to minimize pumping cost subject to maintaining water pressures in allowable ranges at all points in a pipe network can be solved via separation of the variables. If the flow rates are fixed then the subproblem of finding optimal values for the pressure gaps is an easy-to- solve linear minimization problem. With this approach the outer problem of finding optimal values for the flow variables is a nonsmooth problem whose solution can be found efficiently via the techniques to be developed. The computational methods proposed for development are significant because they can be applied in many practical decision-making situations. These include decomposition of large or complicated models such as those occurring in allocation of scarce resources, traffic assignment, manufacturing, structural engineering, logistics and strategic planning. The pipe network problem in the first paragraph is an important example occurring in civil infrastructure design. It illustrates the need to make trade-off decisions when considering conflicting design criteria such as simultaneously having high reliability and low cost. Another area of applica tion is in power system planning subject to environmental protection constraints. These decision models will increase in importance as deregulation of the energy supply sector takes effect. These problems naturally decompose into higher level problems of deciding which types and sizes of power generation plants to build and lower level problems of deciding how best to operate the various units to meet daily energy demand. Additionally, this research fits in well with ongoing efforts in high-performance computing, because its methods will utilize parallel processing to solve larger or more complex decision- making problems than are currently possible to solve.