你好! Shipping to Taiwan with premium packaging for just NT$300 

Ship to
Taiwan
0

My Account

  • argentina
  • chile
  • colombia
  • españa
  • méxico
  • perú
  • estados unidos
  • internacional

menu

Select your country

Americas

Europe

Rest of the world

portada Modern Optimization Methods for Decision Making Under Risk and Uncertainty
Type
Physical Book
Publisher
Author
Language
English
Pages
380
Format
Hardcover
ISBN13
9781032196411
Edition No.
1

Modern Optimization Methods for Decision Making Under Risk and Uncertainty

Gaivoronski Alexei A.,S. Knopov Pavlo,A. Zaslavskyi Volodymyr (Author) · Crc Press · Hardcover

Modern Optimization Methods for Decision Making Under Risk and Uncertainty - Gaivoronski Alexei A.,S. Knopov Pavlo,A. Zaslavskyi Volodymyr

Cheaper New Book Imported to Taiwan
Delivery: 01 Oct - 14 Oct Shipping: 18 to 22 business days.
NT$ 5,680
Faster New Book Imported to Taiwan
Delivery: 24 Sep - 02 Oct Shipping: 13 to 14 business days.
NT$ 8,063
NT$ 5,680

Choose the list to add your product or create one New List

Go to My Wishlists
Calculate shipping cost

Synopsis "Modern Optimization Methods for Decision Making Under Risk and Uncertainty "

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.

Customers reviews

More customer reviews
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)
  • 0% (0)

Frequently Asked Questions about the Book

Is the book original?

All books in our catalog are Original.

In what language is the book written?

The book is written in English.

What is the format of this book?

The binding of this edition is Hardcover.

Questions and Answers about the Book

Do you have a question about the book? Login to be able to add your own question.

Opinions about Bookdelivery

More customer reviews