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DSC-03: Mathematics For Computing - Practicals

Course

Welcome to the collection of practical assignments for the Mathematics For Computing course. This repository contains implementations in both Maxima and Python.

📅 Session: In Maxima

  1. Create And Transform Vectors And Matrices (The Transpose Vector (Matrix) Conjugate Transpose Of A Vector (Matrix)).
  2. Generate the matrix into echelon form and find its rank.
  3. Find Cofactors, Determinant, Adjoint And Inverse Of A Matrix.
  4. Solve A System Of Homogeneous And Non-Homogeneous Equations Using Gauss Elimination Method.
  5. Solve A System Of Homogeneous Equations Using The Gauss Jordan Method.
  6. Generate Basis Of Column Space, Null Space, Row Space And Left Null Space Of A Matrix Space.
  7. Check The Linear Dependence Of Vectors. Generate A Linear Combination Of Given Vectors Of Rn/ Matrices Of The Same Size And Find The Transition Matrix Of Given Matrix Space.
  8. Find The Orthonormal Basis Of Given Vectorspace Using The Gram-Schmidt Orthogonalization Process.
  9. Check The Diagonalizable Property Of Matrices And Find The Corresponding Eigenvalue And Verify The Cayley- Hamilton Theorem.
  10. Application Of Linear Algebra: Coding And Decoding Of Messages Using Non singular Matrices. Eg Code “Linear Algebra Is Fun” And Then Decode It.
  11. Compute Gradient Of A Scalar Field.
  12. Compute Divergence Of A Vector Field.
  13. Compute Curl Of A Vector Field.

📅 Session: In Python

  1. Create And Transform Vectors And Matrices (The Transpose Vector (Matrix) Conjugate Transpose Of A Vector (Matrix)).
  2. Generate the matrix into echelon form and find its rank.
  3. Find Cofactors, Determinant, Adjoint And Inverse Of A Matrix.
  4. Solve A System Of Homogeneous And Non-Homogeneous Equations Using Gauss Elimination Method.
  5. Solve A System Of Homogeneous Equations Using The Gauss Jordan Method.
  6. Generate Basis Of Column Space, Null Space, Row Space And Left Null Space Of A Matrix Space.
  7. Check The Linear Dependence Of Vectors. Generate A Linear Combination Of Given Vectors Of Rn/ Matrices Of The Same Size And Find The Transition Matrix Of Given Matrix Space.
  8. Find The Orthonormal Basis Of Given Vectorspace Using The Gram-Schmidt Orthogonalization Process.
  9. Check The Diagonalizable Property Of Matrices And Find The Corresponding Eigenvalue And Verify The Cayley- Hamilton Theorem.
  10. Application Of Linear Algebra: Coding And Decoding Of Messages Using Non singular Matrices. Eg Code “Linear Algebra Is Fun” And Then Decode It.
  11. Compute Gradient Of A Scalar Field.
  12. Compute Divergence Of A Vector Field.
  13. Compute Curl Of A Vector Field.

<p align="right">

<i>Developed with ❤️ by <a href="https://github.com/16ratneshkumar">16ratneshkumar</a></i>

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