Modern Optimization Methods for Decision Making Under Risk and Uncertainty
暫譯: 現代風險與不確定性下的決策優化方法

Gaivoronski, Alexei A., S. Knopov, Pavlo, A. Zaslavskyi, Volodymyr

Modern Optimization Methods for Decision Making Under Risk and Uncertainty
  • 出版商: CRC
  • 出版日期: 2025-01-29
  • 售價: $2,810
  • 貴賓價: 9.5 折 $2,670
  • 語言: 英文
  • 頁數: 380
  • 裝訂: Quality Paper - also called trade paper
  • ISBN: 1032196432
  • ISBN-13: 9781032196435

    • 海外代購書籍(需單獨結帳)

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商品描述

The book comprises original articles on topical issues of risk theory, rational decision making, statistical decisions, and control of stochastic systems. The articles are the outcome of a series international projects involving the leading scholars in the field of modern stochastic optimization and decision making. The structure of stochastic optimization solvers is described. The solvers in general implement stochastic quasi-gradient methods for optimization and identification of complex nonlinear models. These models constitute an important methodology for finding optimal decisions under risk and uncertainty. While a large part of current approaches towards optimization under uncertainty stems from linear programming (LP) and often results in large LPs of special structure, stochastic quasi-gradient methods confront nonlinearities directly without need of linearization. This makes them an appropriate tool for solving complex nonlinear problems, concurrent optimization and simulation models, and equilibrium situations of different types, for instance, Nash or Stackelberg equilibrium situations. The solver finds the equilibrium solution when the optimization model describes the system with several actors. The solver is parallelizable, performing several simulation threads in parallel. It is capable of solving stochastic optimization problems, finding stochastic Nash equilibria, and of composite stochastic bilevel problems where each level may require the solution of stochastic optimization problem or finding Nash equilibrium. Several complex examples with applications to water resources management, energy markets, pricing of services on social networks are provided. In the case of power system, regulator makes decision on the final expansion plan, considering the strategic behavior of regulated companies and coordinating the interests of different economic entities. Such a plan can be an equilibrium - a planned decision where a company cannot increase its expected gain unilaterally.

商品描述(中文翻譯)

本書包含有關風險理論、理性決策、統計決策及隨機系統控制的原創文章。這些文章是涉及現代隨機優化和決策領域的領先學者所參與的一系列國際項目的成果。書中描述了隨機優化求解器的結構。這些求解器通常實現隨機準梯度方法，用於優化和識別複雜的非線性模型。這些模型構成了一種重要的方法論，用於在風險和不確定性下尋找最佳決策。雖然當前許多針對不確定性下優化的方法源自線性規劃（LP），並且通常導致具有特殊結構的大型線性規劃問題，但隨機準梯度方法直接面對非線性問題，而無需線性化。這使得它們成為解決複雜非線性問題、並行優化和模擬模型以及不同類型的均衡情況（例如，納什均衡或斯塔克伯格均衡情況）的合適工具。當優化模型描述系統中有多個參與者時，求解器會找到均衡解。該求解器是可並行化的，能夠同時執行多個模擬線程。它能夠解決隨機優化問題，尋找隨機納什均衡，以及處理複合隨機雙層問題，其中每一層可能需要解決隨機優化問題或尋找納什均衡。書中提供了幾個複雜的例子，應用於水資源管理、能源市場、社交網絡服務定價等領域。在電力系統的情況下，監管機構根據受監管公司的戰略行為和協調不同經濟實體的利益，對最終擴展計劃做出決策。這樣的計劃可以是一種均衡——一個計劃決策，其中公司無法單方面增加其預期收益。

作者簡介

Alexei A. Gaivoronski obtained Master and PhD degrees in operations research from Moscow University of Physics and Technology (1977 and 1980). His research interests are optimization under uncertainty, risk management, information economics with applications to finance, energy, telecommunications where he published more than 80 research papers. He is Professor of industrial economics and operations research in Norwegian University of Science and Technology. Previously he worked in academia and industry in Ukraine, Austria, France and Italy.

Pavel S. Knopov is the Head of Department, Mathematical Methods of Operation Research at the V.M. Glushkov Institute of Cybernetics National Academy of Science of Ukraine and Professor of National Taras Shevchenko University, Kiev, Ukraine. His research interest are stochastic optimization problems, problems of estimation of random processes and fields, control of random processes and field(s), Risk theory. He has supervised Master and PhD students and been a leader of several international projects.

Volodymyr A. Zaslavskyi is a Doctor of Sciences (Technologies), Professor of Department of Mathematical Informatics, Faculty of Computer Science and Cybernetics, Taras Shevchenko National University of Kyiv. His research interest includes risk analyses and optimization, systems analyses and type-variety principle applications, optimal redundancy and life cycle of critical systems. Guarantee of MAP "Business Informatics", He has supervisor of Master and PhD students, and been Leader of several international projects.

作者簡介(中文翻譯)

阿列克謝·A·蓋沃羅斯基於1977年和1980年在莫斯科物理技術大學獲得運籌學碩士和博士學位。他的研究興趣包括不確定性下的優化、風險管理、信息經濟學，並應用於金融、能源和電信領域，發表了超過80篇研究論文。他是挪威科技大學的工業經濟學和運籌學教授。之前，他曾在烏克蘭、奧地利、法國和意大利的學術界和工業界工作。

帕維爾·S·克諾波夫是烏克蘭國家科學院V.M. 格盧什科夫網絡學研究所運籌學數學方法系的系主任，以及烏克蘭基輔塔拉斯·舍甫琴科國立大學的教授。他的研究興趣包括隨機優化問題、隨機過程和場的估計問題、隨機過程和場的控制、風險理論。他指導過碩士和博士生，並領導過幾個國際項目。

弗拉基米爾·A·扎斯拉夫斯基是科技博士，基輔塔拉斯·舍甫琴科國立大學計算機科學與網絡學院數學信息學系的教授。他的研究興趣包括風險分析與優化、系統分析及類型-變異原則的應用、關鍵系統的最佳冗餘和生命週期。他是MAP「商業信息學」的保證人，指導過碩士和博士生，並領導過幾個國際項目。

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