Refine
Year of publication
Document Type
- ZIB-Report (22)
- Article (2)
- In Proceedings (1)
Is part of the Bibliography
- no (25)
Keywords
- operative planning (3)
- KKT recursion (2)
- Multistage Stochastic Programs (2)
- discrete dynamics (2)
- tree-sparse QP (2)
- Buchungsvalidierung (1)
- Chemical Processes (1)
- Convex program (1)
- Drinking water supply (1)
- Entry-Exit Model (1)
Institute
- ZIB Allgemein (19)
- Mathematical Optimization (6)
\def\KukaRob {{\sf KUKA IR\,761}} {\small Industrial robots have greatly enhanced the performance of automated manufacturing processes during the last decades. International competition, however, creates an increasing demand to further improve both the accuracy of off-line programming and the resulting cycle times on production lines. To meet these objectives, validated dynamic robot models are required. We describe in detail the development of a generic dynamic model, specialize it to an actual industrial robot \KukaRob, and discuss the problem of dynamic calibration. Efficient and robust trajectory optimization algorithms are then presented which, when integrated into a CAD system, are suitable for routine application in an industrial environment. Our computational results for the \KukaRob\ robot performing a real life transport maneuver show that considerable gains in productivity can be achieved by minimizing the cycle time.}
Scenario tree models of stochastic programs arise naturally under standard nonanticipativity assumptions. We demonstrate how tree-sparse programs cover the general case, with \emph{arbitrary} information constraints. Detailed examples and intuitive interpretations illuminate the basic thoughts behind the abstract but elementary construction.
Tree-Sparse Convex Programs
(2001)
Dynamic stochastic programs are prototypical for optimization problems with an inherent tree structure inducing characteristic sparsity patterns in the KKT systems of interior methods. We propose an integrated modeling and solution approach for such tree-sparse programs. Three closely related natural formulations are theoretically analyzed from a control-theoretic viewpoint and compared to each other. Associated KKT solution algorithms with linear complexity are developed and comparisons to other interior approaches and related problem formulations are discussed.
Operative planning in gas distribution networks leads to large-scale mixed-integer optimization problems involving a hyperbolic PDE defined on a graph. We consider the NLP obtained under prescribed combinatorial decisions---or as relaxation in a branch and bound framework, addressing in particular the KKT systems arising in primal-dual interior methods. We propose a custom solution algorithm using sparse local projections, based on the KKT systems' structual properties induced by the discretized gas flow equations in combination with the underlying network topology. The numerical efficiency and accuracy of the algorithm are investigated, and detailed computational comparisons with a control space method and with the multifrontal solver MA27 are provided.
Unnecessarily conservative behavior of standard process control techniques can be avoided by stochastic programming models when the distribution of random disturbances is known. In an earlier study we have investigated such an approach for tank level constraints of a distillation process. Here we address techniques that have accelerated the numerical solution of the large and expensive stochastic programs by a factor of six, and then present a refined optimization model for the same application.
The dynamics of pressurized water distribution networks are naturally modeled by differential algebraic equations (DAE). This paper investigates fundamental structural properties of such a DAE model under weak regularity assumptions. The usual partial derivative-based index-1 condition is shown to be necessary and sufficient for several index concepts, as well as sufficient for solvability in a strong sense. Using the physical properties of nonlinear network elements and the inherent saddle point structure of network hydraulics, we then derive purely topological index criteria based on the network graph and the choice of control variables. Several examples illustrate the theoretical results and explore different non-index-1 situations. A brief discussion of the implications for operative planning by discrete time DAE boundary value problems concludes the paper.
Mean-variance portfolio analysis provided the first quantitative treatment of the tradeoff between profit and risk. We investigate in detail the interplay between objective and constraints in a number of single-period variants, including semi-variance models. Particular emphasis is laid on avoiding the penalization of overperformance. The results are then used as building blocks in the development and theoretical analysis of multi-period models based on scenario trees. A key property is the possibility to remove surplus money in future decisions, yielding approximate downside risk minimization.
Increasing demands on industrial robot operation call for optimal motion planning based on dynamic models. The resulting mathematical problems can be handled efficiently by sparse direct boundary value problem methods. Within this framework we propose a new solution technique that is closely related to the conventional kinematic approach: it eliminates the need for numerical integration of differential equations. First optimization results on a real life transport maneuver demonstrate that the technique may save over 50\,\%\ computation time.
Multistage stochastic programs can be seen as discrete optimal control problems with a characteristic dynamic structure induced by the scenario tree. To exploit that structure, we propose a highly efficient dynamic programming recursion for the computationally intensive task of KKT systems solution within an interior point method. Test runs on a multistage portfolio selection problem demonstrate the performance of the algorithm.
The paper presents a new algorithmic approach for multistage stochastic programs which are seen as discrete optimal control problems with a characteristic dynamic structure induced by the scenario tree. To exploit that structure, we propose a highly efficient dynamic programming recursion for the computationally intensive task of KKT systems solution within a primal-dual interior point method. Convergence is drastically enhanced by a successive refinement technique providing both primal and dual initial estimates. Test runs on a multistage portfolio selection problem demonstrate the performance of the method.