Do a google search to look for a Python timing function. One possibility comes with the...

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Do a google search to look for a Python timing function. One possibility comes with the basic Python distribution in a module called time. The call time.perf_counter() sets a start point and is used with a second time.perf_counter() call. If set to a variable you can take the difference and determine how long a particular section of code takes to execute. 1. Use the text's code for gaussPivot and LUPivot to write Python scripts for each. Make them functions that you can apply later. Include them in a single module for ease of use later. Use the notes description of Cramer's rule to write a Python script to implement it. 3. For text Problem Set 2.2, problem 10 2. a. Solve with all 3 direct methods. b. Verify solution accuracy. (Multiply the matrix times the solution and compute its difference from the RHS-it should be zero.) Compare the performance of each method (make a table) C. 4. Set up nonsingular 2-, 3-, 5-, 8-, and 12-equation systems a. Verify the accuracy of each method. b. Assess the performance of each method by plotting time versus matrix size using Excel. Employ Excel trendlines to determine the equation that the data follows and assess the order of operations for each technique. c. 5. Using LUPivot, create a code that compute the inverse of matrix [A]. a. Test your code using the matrix from text problem 11. b. Use numpy's dot(A,B) function to verify that [A][A]¹¹=[0]. Do a google search to look for a Python timing function. One possibility comes with the basic Python distribution in a module called time. The call time.perf_counter() sets a start point and is used with a second time.perf_counter() call. If set to a variable you can take the difference and determine how long a particular section of code takes to execute. 1. Use the text's code for gaussPivot and LUPivot to write Python scripts for each. Make them functions that you can apply later. Include them in a single module for ease of use later. Use the notes description of Cramer's rule to write a Python script to implement it. 3. For text Problem Set 2.2, problem 10 2. a. Solve with all 3 direct methods. b. Verify solution accuracy. (Multiply the matrix times the solution and compute its difference from the RHS-it should be zero.) Compare the performance of each method (make a table) C. 4. Set up nonsingular 2-, 3-, 5-, 8-, and 12-equation systems a. Verify the accuracy of each method. b. Assess the performance of each method by plotting time versus matrix size using Excel. Employ Excel trendlines to determine the equation that the data follows and assess the order of operations for each technique. c. 5. Using LUPivot, create a code that compute the inverse of matrix [A]. a. Test your code using the matrix from text problem 11. b. Use numpy's dot(A,B) function to verify that [A][A]¹¹=[0].

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Implementation of Gauss Pivot and LUPivot Functions python Copy code import numpy as np def gaussPivotA b n lenb x npzerosn Gaussian elimination for k in rangen 1 Pivot row selection maxindex absAk ka... View the full answer