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Published by Cambridge University Press, 2012
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Published by Cambridge University Press, 2012
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ISBN 10: 1107606608 ISBN 13: 9781107606609
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Published by Cambridge University Press, 2012
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Language: English
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Published by Cambridge University Press, GB, 2012
ISBN 10: 1107606608 ISBN 13: 9781107606609
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Add to basketPaperback. Condition: New. Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff-Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff-Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.
Published by Cambridge University Press, 2012
ISBN 10: 1107606608 ISBN 13: 9781107606609
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Published by Cambridge University Press, 2012
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Published by Cambridge University Press, 2009
ISBN 10: 0521884276 ISBN 13: 9780521884273
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Published by Cambridge University Press, 2012
ISBN 10: 1107606608 ISBN 13: 9781107606609
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Published by Cambridge University Press, GB, 2012
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Add to basketPaperback. Condition: New. Randomized algorithms have become a central part of the algorithms curriculum, based on their increasingly widespread use in modern applications. This book presents a coherent and unified treatment of probabilistic techniques for obtaining high probability estimates on the performance of randomized algorithms. It covers the basic toolkit from the Chernoff-Hoeffding bounds to more sophisticated techniques like martingales and isoperimetric inequalities, as well as some recent developments like Talagrand's inequality, transportation cost inequalities and log-Sobolev inequalities. Along the way, variations on the basic theme are examined, such as Chernoff-Hoeffding bounds in dependent settings. The authors emphasise comparative study of the different methods, highlighting respective strengths and weaknesses in concrete example applications. The exposition is tailored to discrete settings sufficient for the analysis of algorithms, avoiding unnecessary measure-theoretic details, thus making the book accessible to computer scientists as well as probabilists and discrete mathematicians.
Published by Cambridge University Press, 2009
ISBN 10: 0521884276 ISBN 13: 9780521884273
Language: English
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Published by Cambridge University Press, 2009
ISBN 10: 0521884276 ISBN 13: 9780521884273
Language: English
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Published by Cambridge University Press, 2009
ISBN 10: 0521884276 ISBN 13: 9780521884273
Language: English
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Published by Cambridge University Press, 2012
ISBN 10: 1107606608 ISBN 13: 9781107606609
Language: English
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.Über den AutorDevdatt P. Dubhashi is Professor in the Department of Computer Scie.
Published by Cambridge University Press, 2010
ISBN 10: 0521884276 ISBN 13: 9780521884273
Language: English
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Add to basketCondition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book presents a coherent and unified account of classical and more advanced techniques for analyzing the performance of randomized algorithms.Randomized algorithms have become a central part of the algorithms curriculum, based on their increasi.