• Useful packages:
    • R: library(matrixcalc)
    • Python: numpy
  • Matrix generation:
    • R: matrix(1:6, 2, 3)
    • Python: np.arange(1,7).reshape(2,3)
  • construct a diagnal matrix:
    • R: advanced-R
      • diag(vector, nrow, ncol) #construct a matrix which diagonal elements is equal to the vector
      • diag(matrix) # a vector which elements is the diagonal elements of the matrix
    • Python: np.diag(vector)
      • np.diag(vector, k) # like the diag(vector) in R, k means the shift of the diagnoal
      • np.diag(matrix) #return a vector which values are the diagonal elements of the matrix
  • singular value decomposition:
    • R:
      • svd(matrix)
    • Python:
      • np.linalg.svd(matrix, full_matrices=True) #if full_matrices=False, the result will be the same as with R
  • resolve Ax=b
    • R: solve(A,b)
    • Python: np.linalg.solve(A,b)

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