The bisection method for solving the nonlinear bar eigenvalue problem

Samsonov, A. A.; Solov’ev, P. S.; Solov’ev, S. I.

doi:10.1088/1742-6596/1158/4/042011

Cited by 4 publications

(1 citation statement)

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“…A journal that discusses this method in determining eigenvalues is to use a non-linear matrix. This algorithm can localize every problem in determining eigen values [20]. Bolzano theorem said that: 𝑖𝑓 𝑓: [𝑎, 𝑏] ⊂ ℝ → ℝ is a continuous function and if it is holds that 𝑓(𝑎)𝑓(𝑏) < 0, then there is at least one 𝑥𝜖(𝑎, 𝑏) such that 𝑓(𝑥) = 0 [21].…”

Section: Bolzano Methodsmentioning

confidence: 99%

Knowing group motivation using Bolzano method on PageRank computation

2022

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On the e-learning system, student assignments are collected, but problems arise that based on observations from two classes of database courses, 73% of students make plagiarism so lecturers need to give motivation to students. Self motivate is needed by with a group of students. For this reason, using a bolzano method or bisection method will provide an overview of the development of the plagiarism trend between groups of students based on scores on similarity score that compute by PageRank algorithm used by Google. Research method is carried out by conducting preliminary observations of plagiarism scores and creating markov matrix. PageRank compute this ranking and Bolzano method find the intersection of two eigenvalues. Bolzano results the maximum and minimum values on range of intervals where each group consists of 5 students. Experiments were conducted in 3 classes of database courses. Trend analysis found that the plagiarism score averaged 54% with a gradient of 3°, this is relatively small but when spread in different groups it becomes larger and student group have a higher plagiarism score. Results implies a way of looking student motivation based on the plagiarism score by a small groupto motivate each other.

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Section: Bolzano Methodsmentioning

confidence: 99%