Eigenvector - Study guides, Class notes & Summaries

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Lecture 16: Finding Eigenvalues and Eigenvectors
  • Lecture 16: Finding Eigenvalues and Eigenvectors

  • Class notes • 3 pages • 2023
  • Available in package deal
  • Linear Algebra with Applications (Fifth Edition) by Otto Bretsch 7.2 Finding the Eigenvalues of a Matrix 7.3 Finding the Eigenvectors of a Matrix
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Lecture 17: More About Eigenvalues/Eigenvectors
  • Lecture 17: More About Eigenvalues/Eigenvectors

  • Class notes • 3 pages • 2023
  • Available in package deal
  • Linear Algebra with Applications (Fifth Edition) by Otto Bretsch 7.4 More on Dynamical Systems 7.5 Complex Eigenvalues
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Lecture 15: Eigenvalues and Eigenvectors
  • Lecture 15: Eigenvalues and Eigenvectors

  • Class notes • 3 pages • 2023
  • Available in package deal
  • Linear Algebra with Applications (Fifth Edition) by Otto Bretsch 7.1 Diagonalization
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Eigenvector Homework
  • Eigenvector Homework

  • Other • 5 pages • 2023
  • solved homework assignment with eigenvalues, eigenvectors, and determinants
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Test Bank for Linear Algebra and its Applications, 6th
  • Test Bank for Linear Algebra and its Applications, 6th

  • Exam (elaborations) • 175 pages • 2024
  • Test Bank for Linear Algebra and its Applications, 6th Edition Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the system of equations. 1) x 1) 1 - x2 + 3x3 = -8 2x1 + x3 = 0 x1 + 5x2 + x3 = 40 A) (8, 8, 0) B) (0, -8, -8) C) (-8, 0, 0) D) (0, 8, 0) 2) x 2) 1 + 3x2 + 2x3 = 11 4x2 + 9x3 = -12 x3 = -4 A) (1, -4, 6) B) (-4, 1, 6) C) (-4, 6, 1) D) (1, 6, -4) 3) x 3) 1 - x2 +...
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Eigenvalues, Eigenvectors and QR Factorisation
  • Eigenvalues, Eigenvectors and QR Factorisation

  • Class notes • 5 pages • 2023
  • These notes give a heads up and a simple down note about how to proceed with Eigenvalue and Eigenvector calculation as well as showing QR factorisation in practice; this should give you some confidence in tackling the problems and boosting self-confidence in the coming years.
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Test Bank for Linear Algebra and its Applications, 6th
  • Test Bank for Linear Algebra and its Applications, 6th

  • Exam (elaborations) • 175 pages • 2024
  • Test Bank for Linear Algebra and its Applications, 6th Edition Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the system of equations. 1) x 1) 1 - x2 + 3x3 = -8 2x1 + x3 = 0 x1 + 5x2 + x3 = 40 A) (8, 8, 0) B) (0, -8, -8) C) (-8, 0, 0) D) (0, 8, 0) 2) x 2) 1 + 3x2 + 2x3 = 11 4x2 + 9x3 = -12 x3 = -4 A) (1, -4, 6) B) (-4, 1, 6) C) (-4, 6, 1) D) (1, 6, -4) 3) x 3) 1 - x2 +...
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Test Bank for Linear Algebra and its Applications, 6th
  • Test Bank for Linear Algebra and its Applications, 6th

  • Exam (elaborations) • 175 pages • 2024
  • Test Bank for Linear Algebra and its Applications, 6th Edition Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. Solve the system of equations. 1) x 1) 1 - x2 + 3x3 = -8 2x1 + x3 = 0 x1 + 5x2 + x3 = 40 A) (8, 8, 0) B) (0, -8, -8) C) (-8, 0, 0) D) (0, 8, 0) 2) x 2) 1 + 3x2 + 2x3 = 11 4x2 + 9x3 = -12 x3 = -4 A) (1, -4, 6) B) (-4, 1, 6) C) (-4, 6, 1) D) (1, 6, -4) 3) x 3) 1 - x2 +...
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Linear Algebra 1 - Eigenvalues and Eigenvectors. Diagonalisation
  • Linear Algebra 1 - Eigenvalues and Eigenvectors. Diagonalisation

  • Class notes • 21 pages • 2022
  • Available in package deal
  • Notes on how to find the eigenvalues and eigenvectors of a matrix as well as how to determine whether a matrix is diagonalisable.
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IBDP Maths HL IA: Markov Chains & Eigenvector Centrality IBDP Maths HL IA: Markov Chains & Eigenvector Centrality
  • IBDP Maths HL IA: Markov Chains & Eigenvector Centrality

  • Summary • 18 pages • 2020
  • This is a research paper that aims to determine whether a centralized or decentralized passing structure is more beneficial for x school under 16 football team using Markov chains and eigenvector centrality.
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