Euler e os números pentagonais

The present investigation includes a study of Leonhard Euler and the pentagonal numbers is his article Mirabilibus Proprietatibus Numerorum Pentagonalium - E524. After a brief review of the life and work of Euler, we analyze the mathematical concepts covered in that article as well as its historical context. For this purpose, we explain the concept of figurate numbers, showing its mode of generation, as well as its geometric and algebraic representations. Then, we present a brief history of the search for the Eulerian pentagonal number theorem, based on his correspondence on the subject with Daniel Bernoulli, Nikolaus Bernoulli, Christian Goldbach and Jean Le Rond d'Alembert. At first, Euler states the theorem, but admits that he doesn t know to prove it. Finally, in a letter to Goldbach in 1750, he presents a demonstration, which is published in E541, along with an alternative proof. The expansion of the concept of pentagonal number is then explained and justified by compare the geometric and algebraic representations of the new pentagonal numbers pentagonal numbers with those of traditional pentagonal numbers. Then we explain to the pentagonal number theorem, that is, the fact that the infinite product(1 x)(1 xx)(1 x3)(1 x4)(1 x5)(1 x6)(1 x7)... is equal to the infinite series 1 x1 x2+x5+x7 x12 x15+x22+x26 ..., where the exponents are given by the pentagonal numbers (expanded) and the sign is determined by whether as more or less as the exponent is pentagonal number (traditional or expanded). We also mention that Euler relates the pentagonal number theorem to other parts of mathematics, such as the concept of partitions, generating functions, the theory of infinite products and the sum of divisors. We end with an explanation of Euler s demonstration pentagonal number theorem

Comitês de grupamento aplicados a dados de expressão gênica

The main goal of this work is to investigate the suitability of applying cluster ensemble techniques (ensembles or committees) to gene expression data. More specifically, we will develop experiments with three diferent cluster ensembles methods, which have been used in many works in literature: coassociation matrix, relabeling and voting, and ensembles based on graph partitioning. The inputs for these methods will be the partitions generated by three clustering algorithms, representing diferent paradigms: kmeans, ExpectationMaximization (EM), and hierarchical method with average linkage. These algorithms have been widely applied to gene expression data. In general, the results obtained with our experiments indicate that the cluster ensemble methods present a better performance when compared to the individual techniques. This happens mainly for the heterogeneous ensembles, that is, ensembles built with base partitions generated with diferent clustering algorithms