A high-level computing algorithm for diverging and converging branch nonserial dynamic programming systems

AbstractWe present high-level computing algorithms for efficiently processing the diverging and converging branch systems in nonserial dynamic programming. A special technique in devised for processing the network functions such that the minimum amount of storage is employed. It is shown that if k is the discretization level of the state and decision variables then the space complexities are O(k) and O(k2) for the diverging and converging branch systems, respectively. The resultant time complexities are also developed. These savings in computational complexities enhance the attractiveness of dynamic programming as a tool for processing more complex nonserial systems

Global approaches to solving recognition problems of noisy images, 1989

An important problem in the area of pattern recognition is automatic detection of certain pre-assigned elements of an image distorted by noise. In this research, a global ap proach will be used. One such approach is to use an optimal smoothing algorithm which depends on efficient dynamic programming computational techniques. The basic purpose of this research is to make this dynamic programming process efficient in terms of storage requirement and computational effort. Our goal, using the objective function, is to find an optimal order of optimization and then design an effi cient computational technique. Two global techniques will be presented in this paper. Included is a graph-searching technique and the above men tioned technique using dynamic programming. Emphasis will be on the development of an algorithm using dynamic program ming. I wish to express ray deepest appreciation and sincere gratitude to those who have contributed their time, energy, and support to make this study possible. Thanks are especially due Dr. Warsi, Nazir A. , my thesis advisor. His instruction, suggestions, and patience were essential to the completion of this thesi s. Further thanks are also due Nasa Langley for providing financial, technical, and general support to help make this study possible. Special thanks are offered to Mr. Micheal Goode, Technical Monitor, for his technical support and to Dr. Samuel E. Massenberg, University Affairs Officer, for his general support

Gas field scheduling

Woodside Offshore Petroleum is the operator in the development of new gas fields in Australia's North West Shelf project. Sequencing the development of new gas fields in this project is a key determinant of its return on investment. This development sequence has constraints imposed by infrastructure and contractual obligations as well as natural features. The determination of an optimal or very good solution may involve a number of techniques from operations research. The study group attempted several approaches to the problem, principal amongst them being mathematical programming and dynamic programming. A few other heuristic approaches were also considered. The mathematical programming approach was able to yield solutions to small instances of the problem. The group was able to identify several avenues for further research and work on the problem is ongoing

Three Recursive Approaches for Decision Processes with a Converging Branch System

In this paper, we consider a decision process model with a converging branch system that is a nonserial transition system. The model is treated by three approaches. Thus we introduce three types of recursive equations by using a dynamic programming technique

Mutually Dependent Decision Processes Models

We introduce a new framework for dynamic programming called mutually dependent decision processes (MDDPs). Each MDDPs model is constructed from two or more finite-stage deterministic decision processes. At each stage, the reward in one process depends on the optimal values of the other processes, whose initial state is determined by the current state and decision of the original process. We formulate the MDDPs models and derive their mutually dependent recursive equations by dynamic programming