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Stochastic programming is the study of procedures for decision
making under the presence of uncertainties and risks. Stochastic
programming approaches have been successfully used in a number of
areas such as energy and production planning, telecommunications,
and transportation. Recently, the practical experience gained in
stochastic programming has been expanded to a much larger spectrum
of applications including financial modeling, risk management, and
probabilistic risk analysis. Major topics in this volume include:
(1) advances in theory and implementation of stochastic programming
algorithms; (2) sensitivity analysis of stochastic systems; (3)
stochastic programming applications and other related topics.
Audience: Researchers and academies working in optimization,
computer modeling, operations research and financial engineering.
The book is appropriate as supplementary reading in courses on
optimization and financial engineering.
Probabilistic and percentile/quantile functions play an important
role in several applications, such as finance (Value-at-Risk),
nuclear safety, and the environment. Recently, significant advances
have been made in sensitivity analysis and optimization of
probabilistic functions, which is the basis for construction of new
efficient approaches. This book presents the state of the art in
the theory of optimization of probabilistic functions and several
engineering and finance applications, including material flow
systems, production planning, Value-at-Risk, asset and liability
management, and optimal trading strategies for financial
derivatives (options). Audience: The book is a valuable source of
information for faculty, students, researchers, and practitioners
in financial engineering, operation research, optimization,
computer science, and related areas.
Stochastic programming is the study of procedures for decision
making under the presence of uncertainties and risks. Stochastic
programming approaches have been successfully used in a number of
areas such as energy and production planning, telecommunications,
and transportation. Recently, the practical experience gained in
stochastic programming has been expanded to a much larger spectrum
of applications including financial modeling, risk management, and
probabilistic risk analysis. Major topics in this volume include:
(1) advances in theory and implementation of stochastic programming
algorithms; (2) sensitivity analysis of stochastic systems; (3)
stochastic programming applications and other related topics.
Audience: Researchers and academies working in optimization,
computer modeling, operations research and financial engineering.
The book is appropriate as supplementary reading in courses on
optimization and financial engineering.
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