Merging fundamental concepts of analysis and recursion theory to a new exciting theory, this book provides a solid fundament for studying various aspects of computability and complexity in analysis. It is the result of an introductory course given for several years and is written in a style suitable for graduate-level and senior students in computer science and mathematics. Many examples illustrate the new concepts while numerous exercises of varying difficulty extend the material and stimulate readers to work actively on the text.

Merging fundamental concepts of analysis and recursion theory to a new exciting theory, this book provides a solid fundament for studying various aspects of computability and complexity in analysis. It is the result of an introductory course given for several years and is written in a style suitable for graduate-level and senior students in computer science and mathematics. Many examples illustrate the new concepts while numerous exercises of varying difficulty extend the material and stimulate readers to work actively on the text.

Computable analysis is the modern theory of computability and complexity in analysis that arose out of Turing's seminal work in the 1930s. This was motivated by questions such as: which real numbers and real number functions are computable, and which mathematical tasks in analysis can be solved by algorithmic means? Nowadays this theory has many different facets that embrace topics from computability theory, algorithmic randomness, computational complexity, dynamical systems, fractals, and analog computers, up to logic, descriptive set theory, constructivism, and reverse mathematics. In recent decades computable analysis has invaded many branches of analysis, and researchers have studied computability and complexity questions arising from real and complex analysis, functional analysis, and the theory of differential equations, up to (geometric) measure theory and topology. This handbook represents the first coherent cross-section through most active research topics on the more theoretical side of the field. It contains 11 chapters grouped into parts on computability in analysis; complexity, dynamics, and randomness; and constructivity, logic, and descriptive complexity. All chapters are written by leading experts working at the cutting edge of the respective topic. Researchers and graduate students in the areas of theoretical computer science and mathematical logic will find systematic introductions into many branches of computable analysis, and a wealth of information and references that will help them to navigate the modern research literature in this field.

This book addresses major issues such as a growing world energy demand, environmental degradation due to anthropogenic greenhouse gas (GHG) emission, and risk management of disastrous events such as pandemics, abnormal climate, and earthquakes. Using cutting-edge analytical tools, particularly computable general equilibrium (CGE) modelling, the analyses are focused on a very wide range of policy-relevant economic questions for the Asia-Pacific region, especially for Japan, China, India, Vietnam, and smaller nations, including Brunei, Timor Leste, and Fiji. The first part considers (a) the effects of climate change on agriculture sectors, energy policies, and future GHG emission trends, (b) adaptation to climate changes in energy policy and its impacts on the economies, and (c) risk management of catastrophic events such as global pandemics. The second part examines (a) energy environmental issues, (b) economic impacts of natural disaster and depopulation, and (c) effects of informatics development on risk management, using CGE modelling and other methods in regional science fields. Contributors are internationally active leading CGE modellers and environmental economists. The book should be greatly beneficial for scholars and graduate students as well as policy makers who are interested in the economic effects and management of risks relating to climate change and disastrous events.

The book provides a hands-on introduction to computable general equilibrium (CGE) models, written at an accessible, undergraduate level.

The first graduate-level treatment of computable analysis within the tradition of classical mathematical reasoning.

Since their inception, the Perspectives in Logic and Lecture Notes in Logic series have published seminal works by leading logicians. Many of the original books in the series have been unavailable for years, but they are now in print once again. In this volume, the first publication in the Perspectives in Logic series, Pour-El and Richards present the first graduate-level treatment of computable analysis within the tradition of classical mathematical reasoning. The book focuses on the computability or noncomputability of standard processes in analysis and physics. Topics include classical analysis, Hilbert and Banach spaces, bounded and unbounded linear operators, eigenvalues, eigenvectors, and equations of mathematical physics. The work is self-contained, and although it is intended primarily for logicians and analysts, it should also be of interest to researchers and graduate students in physics and computer science.

This superb exposition of a complex subject examines new developments in the theory and practice of computation from a mathematical perspective, with topics ranging from classical computability to complexity, from biocomputing to quantum computing. This book is suitable for researchers and graduate students in mathematics, philosophy, and computer science with a special interest in logic and foundational issues. Most useful to graduate students are the survey papers on computable analysis and biological computing. Logicians and theoretical physicists will also benefit from this book.