Titlebook: Health Promotion for Children and Adolescents; Maya Rom Korin Book 2016 Springer Science+Business Media, LLC, part of Springer Nature 2016

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han what was expected from the worst-case proved bounds, both in terms of the running time and the output quality. In this article, we investigate this puzzling statement by trying to model the average case of lattice reduction algorithms, starting with the celebrated Lenstra-Lenstra-Lovász algorith

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Maya Rom Korin Ph.D., M.S.exactly the closed convex subsets. Furthermore, convex functions are studied, which are closely connected to convex sets and provide a natural generalization of linear functions. Then convex minimization problems are considered. Quadratic programs are examined from a theoretical and practical point

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ent algorithms for optimization problems. Recent advances in algorithmic theory have shown that the traditionally separate areas of discrete optimization, linear programming, and nonlinear optimization are closely linked. This book offers a comprehensive introduction to the whole subject and leads t

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Karol L. Kumpfer Ph.D.,Cátia Magalh?es Ph.D.,Sheetal A. Kanse M.P.H., Ph.D.corresponding defining relations. The theory has its origins and roots in topology and complex analysis and in particular in the theory of the fundamental groups of combinatorial cell complexes. Because of its nature and origins, combinatorial group theory comes into contact with, and uses, many dif

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Eliot Goldman Ph.D.,Joan Stamler Ph.D.,Kimberly Kleinman Ph.D.,Sarah Kerner Psy.D,Owen Lewis M.D.concile this intuition with the fact that both are statistically equally likely? What does it mean to say that an individual mathematical object such as a real number is random, or to say that one real is more random than another? And what is the relationship between randomness and computational pow

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Alexandria B. Fladhammer,Adrian R. Lyde Ph.D.,Adena B. Meyers Ph.D.,Jeffrey K. Clark H.S.D.,Steven Lc.Essential resource for researchers and graduate students iIntuitively, a sequence such as 101010101010101010… does not seem random, whereas 101101011101010100…, obtained using coin tosses, does. How can we reconcile this intuition with the fact that both are statistically equally likely? What does