Matrices are used in many different places: optimisation, system modelling, image manipulation, linear calculations, etc. One...

 

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Matrices are used in many different places: optimisation, system modelling, image manipulation, linear calculations, etc. One aspect of matrix manipulation is the calculation of a 'determinant', which indicates a square-shaped matrix has an inverse if the determinant is not equal to 0. This also steps into Laplace theory, which a lot of you will come across in your studies. The theory to find the determinant for a 2x2 matrix is as follows: A = [ab cd] |A|=ad-bc You'll notice that |A| looks like the same symbology for 'absolute value, but it also means 'the determinate of the matrix A' #justmaththings (a) (0.5 mark): We'll start by creating the following 2D matrix and printing it to the console: A = [12 34] (b) (1.5 mark): Using the above determinant equation, access the values in the matrix to calculate the determinate and print to the console. If done correctly, the expected output is: -2 Matrices are used in many different places: optimisation, system modelling, image manipulation, linear calculations, etc. One aspect of matrix manipulation is the calculation of a 'determinant', which indicates a square-shaped matrix has an inverse if the determinant is not equal to 0. This also steps into Laplace theory, which a lot of you will come across in your studies. The theory to find the determinant for a 2x2 matrix is as follows: A = [ab cd] |A|=ad-bc You'll notice that |A| looks like the same symbology for 'absolute value, but it also means 'the determinate of the matrix A' #justmaththings (a) (0.5 mark): We'll start by creating the following 2D matrix and printing it to the console: A = [12 34] (b) (1.5 mark): Using the above determinant equation, access the values in the matrix to calculate the determinate and print to the console. If done correctly, the expected output is: -2

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To achieve this in Python you can use nested lists to represent a 2x2 matri... View the full answer