الصفحة 1
الصفحة 1
Nonlinear Integer Programming
It is not an exaggeration that much of what people devote in their hfe re solves around optimization in one way or another. On one hand, many decision making problems in real applications naturally result in optimization problems in a form of integer programming. On the other hand, integer programming has been one of the great challenges for the optimization research community for many years, due to its computational difficulties: Exponential growth in its computational complexity with respect to the problem dimension. This book addresses the topic of the general nonlinear integer programming (NLIP). The overall goal of the book is to bring the state of the art of the theoretical foundations and solution methods of NLIP to readers who are interested in optimization, operations research and computer science. This book investigates the theory and solution methodologies for the general NLIP and provides the developments
Fuzzy Choice Functions : A Revealed Preference Approach
This book extends the theory of revealed preference to fuzzy choice functions and provides applications to multicriteria decision making problems. The main topics of revealed preference theory (rationality, revealed preference and congruence axioms, consistency conditions) are treated in the framework of fuzzy choice functions. New topics, such as the degree of dominance and similarity of vague choices, are developed. The results obtained are applied to economic problems where partial information and human subjectivity involve vague choices and vague preferences. The book contains a number of new results achieved by the author. Even though the text is reasonably self-contained, previous knowledge of revealed preference and fuzzy set theory is helpful for the reader.
Markov Decision Processes with Their Applications
Markov decision processes (MDPs), also called stochastic dynamic programming, were first studied in the 1960s. MDPs can be used to model and solve dynamic decision-making problems that are multi-period and occur in stochastic circumstances. There are three basic branches in MDPs: discrete-time MDPs, continuous-time MDPs and semi-Markov decision processes. Starting from these three branches, many generalized MDPs models have been applied to various practical problems. These models include partially observable MDPs, adaptive MDPs, MDPs in stochastic environments, and MDPs with multiple objectives, constraints or imprecise parameters.
