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The input data for data processing algorithms come from measurements and are hence not precise. We therefore need to estimate the accuracy of the results of data processing. It turns out that even for the simplest data processing algorithms, this problem is, in general, intractable. This book describes for what classes of problems interval computations (i.e. data processing with automatic results verification) are feasible, and when they are intractable. This knowledge is important, e.g. for algorithm developers, because it will enable them to concentrate on the classes of problems for which general algorithms are possible.
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Subjects
Computational complexity, Interval analysis (Mathematics), Numerical calculations, Data processing, General Theory of Computing, Numerical Analysis, Interval analysis (Mathematics, Theory Of Computing, Science/Mathematics, Mathematics, Applied, Data Processing - General, Mathematical Analysis, Computers-Data Processing - General, Mathematics / Mathematical Analysis, Mathematics / Number Systems, Mathematics-Applied, Probability & Statistics - General, Information theory, Computer science, Mathematical optimization, Computational Mathematics and Numerical Analysis, Theory of Computation, Optimization, Mathematical Modeling and Industrial Mathematics, Applications of MathematicsShowing 4 featured editions. View all 4 editions?
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1
Computational Complexity and Feasibility of Data Processing and Interval Computations
2013, Springer London, Limited
in English
1475727933 9781475727937
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2
Computational Complexity and Feasibility of Data Processing and Interval Computations
Dec 10, 2010, Springer, Springer US
paperback
144194785X 9781441947857
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3
Computational complexity and feasibility of data processing and interval computations
1998, Kluwer Academic Publishers
in English
0792348656 9780792348658
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4
Computational Complexity and Feasibility of Data Processing and Interval Computations (Applied Optimization)
December 31, 1997, Springer
Hardcover
in English
- 1 edition
0792348656 9780792348658
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