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Vladik Kreinovich, Anatoly Lakeyev, Jiri Rohn, Patrick Kahl
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.
| Publisher | Springer, Springer US |
|---|---|
| Pages | 471 |
| Format | paperback |
| Search language | english |
| ISBN_10 | 1-441-94785-X primary |
| ISBN_13 | 978-1-441-94785-7 primary |
Publication-specific alternatives linked to the same work.
Computational complexity and feasibility of data processing and interval computations
Computational Complexity and Feasibility of Data Processing and Interval Computations (Applied Optimization)
Computational Complexity and Feasibility of Data Processing and Interval Computations