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This book contains papers presented at a workshop on the use of parallel techniques in symbolic and algebraic computation held at Cornell University in May 1990. The eight papers in the book fall into three groups. The first three papers discuss particular programming substrates for parallel symbolic computation, especially for distributed memory machines. The next three papers discuss novel ways of computing with elements of finite fields and with algebraic numbers. The finite field technique is especially interesting since it uses the Connection Machine, a SIMD machine, to achievesurprising amounts of parallelism. One of the parallel computing substrates is also used to implement a real root isolation technique. One of the crucial algorithms in modern algebraic computation is computing the standard, or Gr|bner, basis of an ideal. The final two papers discuss two different approaches to speeding their computation. One uses vector processing on the Cray and achieves significant speed-ups. The other uses a distributed memory multiprocessor and effectively explores the trade-offs involved with different interconnect topologies of the multiprocessors.
In this book, we study theoretical and practical aspects of computing methods for mathematical modelling of nonlinear systems. A number of computing techniques are considered, such as methods of operator approximation with any given accuracy; operator interpolation techniques including a non-Lagrange interpolation; methods of system representation subject to constraints associated with concepts of causality, memory and stationarity; methods of system representation with an accuracy that is the best within a given class of models; methods of covariance matrix estimation; methods for low-rank matrix approximations; hybrid methods based on a combination of iterative procedures and best operator approximation; and methods for information compression and filtering under condition that a filter model should satisfy restrictions associated with causality and different types of memory. As a result, the book represents a blend of new methods in general computational analysis, and specific, but also generic, techniques for study of systems theory ant its particular branches, such as optimal filtering and information compression. - Best operator approximation, - Non-Lagrange interpolation, - Generic Karhunen-Loeve transform - Generalised low-rank matrix approximation - Optimal data compression - Optimal nonlinear filtering
Fundamentals of Computing and Computer Programming (updated edition) builds on the strengths of the first edition. It now provides four new appendices containing solved problems in flowcharts, algorithms, pseudo codes and number systems. It also contains 30 new solved C programs with flowcharts for better understanding. Written in a lucid style, it provides numerous examples and rich pedagogical aids, which makes learning easier for the reader. It begins with a chapter on Introduction to Computers, followed by chapters on ‘Computer Software’ and ‘Problem Solving & Office Automation’. The fourth chapter introduces the readers to C programming with the fifth chapter covering the role of ‘Functions and Pointers’ in C.
Prepared for the Office of Science and Technology Policy and the National Science Foundation, these briefings examine areas important to the progress of U.S. science and technology: the science of interfaces and thin films, decision making and problem solving, protein structure and biological function, and the prevention and treatment of viral diseases.
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