Noisy Information and Computational Complexity
Noisy Information and Computational Complexity
In this volume, which was originally published in 1996, noisy information is studied in the context of computational complexity; in other words the text deals with the computational complexity of mathematical problems for which information is partial, noisy and priced. The author develops a general theory of computational complexity of continuous problems with noisy information and gives a number of applications; deterministic as well as stochastic noise is considered. He presents optimal algorithms, optimal information, and complexity bounds in different settings: worst case, average case, mixed worst-average and average-worst, and asymptotic. The book integrates the work of researchers in such areas as computational complexity, approximation theory and statistics, and includes many fresh results as well. About two hundred exercises are supplied with a view to increasing the reader's understanding of the subject. The text will be of interest to professional computer scientists, statisticians, applied mathematicians, engineers, control theorists, and economists.
- Was the first book where noisy information is studied in the context of computational complexity
- Integrates results of researchers from several fields: computational complexity, approximation theory and statistics
- Can be used as an advanced textbook
Product details
March 2012Paperback
9780521349444
322 pages
229 × 152 × 18 mm
0.48kg
Available
Table of Contents
- 1. Overview
- 2. Worst case setting
- 3. Average case setting
- 4. Worst-average case setting
- 5. Average-worst case setting
- 6. Asymptotic setting
- Bibliography
- Glossary
- Indices.
