To examine, analyze, and manipulate a problem to the point of designing an algorithm for solving it is an exercise of fundamental value in many fields. With so many everyday activities governed by algorithmic principles, the power, precision, reliability and speed of execution demanded by users have transformed the design and...
Somebody once said that one may prove the correctness of an algorithm, but not of a
program. One of the main goals of this book is to convince the reader that things are
not so bad.
A well-known programmer, C.A.R. Hoare, said that the beauty of a program is
not an additional benefit but a criterion...
Markov chains are a particularly powerful and widely used tool for analyzing a variety of stochastic (probabilistic) systems over time. This monograph will present a series of Markov models, starting from the basic models and then building up to higher-order models. Included in the higher-order discussions are multivariate models, higher-order...
Evolutionary algorithms are becoming increasingly attractive across various disciplines, such as operations research, computer science, industrial engineering, electrical engineering, social science and economics. Introduction to Evolutionary Algorithms presents an insightful, comprehensive, and up-to-date treatment of evolutionary algorithms....
Based on their extensive experience with teaching R and statistics to applied scientists, the authors provide a beginner's guide to R. To avoid the difficulty of teaching R and statistics at the same time, statistical methods are kept to a minimum.
Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems.
Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects...
“If you torture the data long enough, Nature will confess,” said 1991 Nobel-winning economist
Ronald Coase. The statement is still true. However, achieving this lofty goal is not easy. First,
“long enough” may, in practice, be “too long” in many applications and thus unacceptable. Second,...
Markov decision process (MDP) models are widely used for modeling sequential decision-making problems that arise in engineering, economics, computer science, and the social sciences. Many real-world problems modeled by MDPs have huge state and/or action spaces, giving an opening to the curse of dimensionality and so making practical...
One of the current main challenges in the area of scientific computing? is the design and implementation of accurate numerical models for complex physical systems which are described by time dependent coupled systems of nonlinear PDEs. This volume integrates the works of experts in computational...
Database and information systems technologies have been rapidly evolving in several directions over the past years. New types and kinds of data, new types of applications and information systems to support them raise diverse challenges to be addressed. The so-called big data challenge, streaming data management and processing, social networks...
Modern computing tools like MAPLE (a symbolic computation package)
and MATLAB (a numeric computation and visualization program)
make it possible to use the techniques of scientific computing
to solve realistic nontrivial problems in a classroom setting. These...
This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated...
