Algorithm Design Tordos

….The most comprehensive guide to designing practical and efficient algorithms!….
The Algorithm Design Manual, Second Edition
"…the book is an algorithm-implementation treasure trove, and putting all of these implementations in one place was no small feat. The list of implementations [and] extensive bibliography make the book an invaluable resource for everyone interested in the subject." –ACM Computing Reviews

For a long time the design of data processing systems has been mainly based on experience and practical considerations more than on formal quantitative approaches. This was true both in the design of computer architecture and in the design of software systems such as operating systems and database systems. Two relevant exceptions were the early studies in switching theory, concerned with such problems as minimization and reliability, on the one hand, and on the other hand, the general mathematical approach to computer system modeling and performance evaluation. More recently, the evaluation of distributed computing related to technological advances in microelectronics, has increased the need for quantitative studies for optimizing the design of computer systems.

Networked information technology (NIT) systems are synonymous with network-centric or net-centric systems and constitute the cornerstone of the fast approaching information age. To date, however, the design and development of NIT systems have been ad hoc and have suffered from a dearth of supporting scientific and theoretical principles.

Over the course of a generation, algorithms have gone from mathematical abstractions to powerful mediators of daily life. Algorithms have made our lives more efficient, more entertaining, and, sometimes, better informed. At the same time, complex algorithms are increasingly violating the basic rights of individual citizens. Allegedly anonymized datasets routinely leak our most sensitive personal information; statistical models for everything from mortgages to college admissions reflect racial and gender bias. Meanwhile, users manipulate algorithms to "game" search engines, spam filters, online reviewing services, and navigation apps.

Randomness is a powerful phenomenon that can be harnessed to solve various problems in all areas of computer science. Randomized algorithms are often more efficient, simpler and, surprisingly, also more reliable than their deterministic counterparts. Computing tasks exist that require billions of years of computer work when solved using the fastest known deterministic algorithms, but they can be solved using randomized algorithms in a few minutes with negligible error probabilities.

Despite five decades of research, parallel computing remains an exotic, frontier technology on the fringes of mainstream computing. Its much-heralded triumph over sequential computing has yet to materialize. This is in spite of the fact that the processing needs of many signal processing applications continue to eclipse the capabilities of sequential computing. The culprit is largely the software development environment. Fundamental shortcomings in the development environment of many parallel computer architectures thwart the adoption of parallel computing. Foremost, parallel computing has no unifying model to accurately predict the execution time of algorithms on parallel architectures. Cost and scarce programming resources prohibit deploying multiple algorithms and partitioning strategies in an attempt to find the fastest solution. As a consequence, algorithm design is largely an intuitive art form dominated by practitioners who specialize in a particular computer architecture. This, coupled with the fact that parallel computer architectures rarely last more than a couple of years, makes for a complex and challenging design environment.