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Stochastic Programming
This book shows the breadth and depth of stochastic programming applications. All the papers presented here involve optimization over the scenarios that represent possible future outcomes of the uncertainty problems. The applications, which were presented at the 12th International Conference on Stochastic Programming held in Halifax, Nova Scotia in August 2010, span the rich field of uses of these models. The finance papers discuss such diverse problems as longevity risk management of individual investors, personal financial planning, intertemporal surplus management, asset management with benchmarks, dynamic portfolio management, fixed income immunization and racetrack betting. The production and logistics papers discuss natural gas infrastructure design, farming Atlantic salmon, prevention of nuclear smuggling and sawmill planning. The energy papers involve electricity production planning, hydroelectric reservoir operations and power generation planning for liquid natural gas plants. Finally, two telecommunication papers discuss mobile network design and frequency assignment problems.
Stochastic Optimization Methods in Finance and Energy
This volume presents a collection of contributions dedicated to applied problems in the financial and energy sectors that have been formulated and solved in a stochastic optimization framework. The invited authors represent a group of scientists and practitioners, who cooperated in recent years to facilitate the growing penetration of stochastic programming techniques in real-world applications, inducing a significant advance over a large spectrum of complex decision problems. After the recent widespread liberalization of the energy sector in Europe and the unprecedented growth of energy prices in international commodity markets, we have witnessed a significant convergence of strategic decis...
Decision Making Under Uncertainty
In the ideal world, major decisions would be made based on complete and reliable information available to the decision maker. We live in a world of uncertainties, and decisions must be made from information which may be incomplete and may contain uncertainty. The key mathematical question addressed in this volume is "how to make decision in the presence of quantifiable uncertainty." The volume contains articles on model problems of decision making process in the energy and power industry when the available information is noisy and/or incomplete. The major tools used in studying these problems are mathematical modeling and optimization techniques; especially stochastic optimization. These articles are meant to provide an insight into this rapidly developing field, which lies in the intersection of applied statistics, probability, operations research, and economic theory. It is hoped that the present volume will provide entry to newcomers into the field, and stimulation for further research.
Investment Risk Management
Investment Risk Management provides an overview of developments in risk management and a synthesis of research on the subject. The chapters examine ways to alter exposures through measuring and managing risk exposures and provide an understanding of the latest strategies and trends within risk management.
Modeling, Simulation, and Optimization of Integrated Circuits
The third Conference on Mathematical Models and Numerical Simulation in Electronic Industry brought together researchers in mathematics, electrical engineering and scientists working in industry. The contributions to this volume try to bridge the gap between basic and applied mathematics, research in electrical engineering and the needs of industry.
Efficient Transient Noise Analysis in Circuit Simulation
The current technological progress in microelectronics is driven by the desire to decrease feature sizes, increase frequencies and the need for low supply voltages. Amongst other effects the signal-to-noise ratio decreases and the transient noise analysis becomes necessary in the simulation of electronic circuits. Taking the inner electronic noise into account by means of Gaussian white noise currents, mathematical modelling leads to stochastic differential algebraic equations (SDAEs) with a large number of small noise sources. The simulation of such systems requires an efficient numerical time integration by mean-square convergent numerical methods. In this thesis, adaptive linear multi-step Maruyama schemes to solve stochastic differential equations (SDEs) and SDAEs are developed. A reliable local error estimate for systems with small noise is provided and a strategy for controlling the step-size and the number of solution paths simultaneously in one approximation is presented. Numerical experiments on industrial relevant real-life applications illustrate the theoretical findings.
German Success Stories in Industrial Mathematics
This book should illustrate the impact of collaborations between mathematics and industry. It is both an initiative of and coordinated by the German Committee for Mathematical Modeling, Simulation and Optimization (KoMSO). This publication aims at comparing the state of the art at the intersection of mathematics and industry, as well as the demands for future development of science and technology in Germany and beyond. Each contribution addresses the importance of mathematics in innovation by means of introducing a successful cooperation with an industrial partner in order to display the wide range of industrial sectors where the use of mathematics is the crucial factor for success, but also show the variety of mathematical areas involved in these activities. The success stories introduced in this volume will be supplemented by appropriate illustrations. It is the goal of this publication to highlight cooperation between mathematics and industry as a two-way technology and knowledge transfer, providing industry with solutions and mathematics with new research topics and inspiring new methodologies.
