# Randomized algorithms approximation generation and counting

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BY **Russ Bubley**

1852333251 Shared By Guest

**Randomized algorithms approximation generation and counting** Russ Bubley is available to download <table><tr><td colspan="2"><strong style="font-size:1.This material is available do download at niSearch.com on **Russ Bubley**'s eBooks, 2em;">Randomized algorithms approximation generation and counting</strong><br/>Russ Bubley</td></tr> <tr> <td><b>Type:</b></td> <td>eBook</td> </tr> <tr> <td><b>Released:</b></td> <td>2000</td> </tr> <tr> <td><b>Publisher:</b></td> <td>Springer</td> </tr> <tr> <td><b>Page Count:</b></td> <td>166</td> </tr> <tr> <td><b>Format:</b></td> <td>djvu</td> </tr> <tr> <td><b>Language:</b></td> <td>English</td> </tr> <tr> <td><b>ISBN-10:</b></td> <td>1852333251</td> </tr> <tr> <td><b>ISBN-13:</b></td> <td>9781852333256</td> </tr> </table>
Randomized Algorithms discusses two problems of fine pedigree: counting and generation, both of which are of fundamental importance to discrete mathematics and probability.*Randomized algorithms approximation generation ...* Textbook When asking questions like "How many are there?" and "What does it look like on average?" of families of combinatorial structures, answers are often difficult to find -- we can be blocked by seemingly intractable algorithms. Randomized Algorithms shows how to get around the problem of intractability with the Markov chain Monte Carlo method, as well as highlighting the method's natural limits. It uses the technique of coupling before introducing "path coupling" a new technique which radically simplifies and improves upon previous methods in the area.

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