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)
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.
\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.}
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.
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.
Das aus den Medien bekannte umstrittene Ziegenproblem (auch Drei-Türen-Problem genannt) wird vollständig analysiert und gelöst. In der Streitfrage spielen sprachliche Mehrdeutigkeiten der Problemformulierung eine wesentliche Rolle; zudem werden Zufallsereignisse mit willkürlicher Information über deren Ergebnisse verwechselt. Tatsächlich erweisen sich beide strittigen Lösungen als teilweise richtige Bestandteile der Gesamtlösung. Die Argumentation wird in allgemeinverständlicher Sprache geführt und anschliessend durch eine formale mathematische Betrachtung ergänzt.
Interior point methods for multistage stochastic programs involve KKT systems with a characteristic global block structure induced by dynamic equations on the scenario tree. We generalize the recursive solution algorithm proposed in an earlier paper so that its linear complexity extends to a refined tree-sparse KKT structure. Then we analyze how the block operations can be specialized to take advantage of problem-specific sparse substructures. Savings of memory and operations for a financial engineering application are discussed in detail.
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.
Standard model predictive control for real-time operation of industrial production processes may be inefficient in the presence of substantial uncertainties. To avoid overly conservative disturbance corrections while ensuring safe operation, random influences should be taken into account explicitly. We propose a multistage stochastic programming approach within the model predictive control framework and apply it to a distillation process with a feed tank buffering external sources. A preliminary comparison to a probabilistic constraints approach is given and first computational results for the distillation process are presented.
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.