Record ID | marc_loc_2016/BooksAll.2016.part37.utf8:139756225:2281 |
Source | Library of Congress |
Download Link | /show-records/marc_loc_2016/BooksAll.2016.part37.utf8:139756225:2281?format=raw |
LEADER: 02281cam a22003374a 4500
001 2010023587
003 DLC
005 20101130084730.0
008 100608s2010 nyua b 001 0 eng
010 $a 2010023587
020 $a9780521192484 (hardback)
020 $a052119248X (hardback)
020 $a9780521122542 (pbk.)
020 $a0521122546 (pbk.)
035 $a(OCoLC)ocn642204747
040 $aDLC$cDLC$dYDX$dCDX$dYDXCP$dDLC
042 $apcc
050 00 $aQA267.7$b.G652 2010
082 00 $a005.1$222
100 1 $aGoldreich, Oded.
245 10 $aP, NP, and NP-completeness :$bthe basics of computational complexity /$cOded Goldreich.
260 $aNew York :$bCambridge University Press,$c2010.
300 $axxix, 184 p. :$bill. ;$c24 cm.
520 $a"The focus of this book is the P-versus-NP Question and the theory of NP-completeness. It also provides adequate preliminaries regarding computational problems and computational models. The P-versus-NP Question asks whether or not finding solutions is harder than checking the correctness of solutions. An alternative formulation asks whether or not discovering proofs is harder than verifying their correctness. It is widely believed that the answer to these equivalent formulations is positive, and this is captured by saying that P is different from NP. Although the P-versus-NP Question remains unresolved, the theory of NP-completeness offers evidence for the intractability of specific problems in NP by showing that they are universal for the entire class. Amazingly enough, NP-complete problems exist, and furthermore hundreds of natural computational problems arising in many different areas of mathematics and science are NP-complete"--Provided by publisher.
504 $aIncludes bibliographical references and index.
505 8 $aMachine generated contents note: 1. Computational tasks and models; 2. The P versus NP Question; 3. Polynomial-time reductions; 4. NP-completeness; 5. Three relatively advanced topics; Epilogue: a brief overview of complexity theory.
650 0 $aComputational complexity.
650 0 $aComputer algorithms.
650 0 $aApproximation theory.
650 0 $aPolynomials.
856 42 $3Cover image$uhttp://assets.cambridge.org/97805211/92484/cover/9780521192484.jpg