Develop a function to perform Gaussian elimination with complete pivoting. Name your function as gauss_cp.m. Use two permutation vectors to keep track of the row and column interchanges so that you do not need to do any actual row and column interchanges on the coefficient matrix (to reduce memory traffic). You may refer to gauss_pp.m on Canvas for the use of permutation vectors. (a) Attach the printed code to your report. Include sufficient comments. Find a small matrix to test your function and make sure it is delivering the correct answers. (b) We have learned how to compute the number of floating point operations in an algorithm (or code). Following the same idea, set up a formula for the number of comparisons in your code. For a linear system with an n x n coefficient matrix, how many comparisons does your code need? How does it compare with partial pivoting?
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