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- 02. Find a solution to the following recrce egation T(n)If matrix A is a 2X3 matrix, it can be multiplied by matrix B to obtain AB only if matrix B has:A. 2 rowsB. 2 columnsC. 3 rowsD. 3 columnsIN PYTHON! Given an n×n matrix A and n×1 right-hand side b, write a function that carries out q iterations of the Gauss elimination outer loop (without pivoting), where q is an integer between 1 and n, and returns the resulting augmented matrix with all below-diagonal elements transformed to zero in the left-most q columns. ##The code format given below may help # def gaussq (A, b, q): '''Apply Gauss elimination (without pivoting) to first q columns of a given linear system Inputs: A, coefficient matrix, size n by n b, vector of constants, n by 1 q, number of columns to apply Gauss elimination to, an integer between 1 and n Output: Aa, augmented matrix with all below-diagonal elements transformed to zero in the left-most q columns, n by n+1 #to check the work
- Please follow the following code format and not coping other codes cuz it doesn't work THANK YOU!IN PYTHON: Given an n×n matrix A and n×1 right-hand side b, write a function that carries out q iterations of the Gauss elimination outer loop (without pivoting), where q is an integer between 1 and n, and returns the resulting augmented matrix with all below-diagonal elements transformed to zero in the left-most q columns. the code format:Write a program that does the following: 1- Ask the user to enter the number of variables on a Linear- System 2- Ask the user to enter matrix elements 3- Ask the user enter vector elements 4- Ask the user to enter initial approximation for the solution 5- Solve the linear-system using Jacobi iteration and show the results and number of iterations needed 6- Solve the linear-system using Gauss-Seidel iteration and show the results and number of iterations needed 7- Show which of the two methods is betterThe following data are given: x= [1,3,5,7]; y=[ln (1), In (3), ln (5), ln (7)] 3. a) Use the interpld function from scipy.interpolate library to interpolate the above data using the spline interpolation method. b) Estimate the logarithm of 2 for linear, quadratic and cubic splines. Print out the true value, interpolating values and the true relative errors in % for linear, quadratic and cubic splines. c) Show on a graph the true function In(x), interpolating linear spline, interpolating quadratic spline, interpolating cubic spline, and data points. Round numbers to four significant digits.
- Write the following set of equations in matrix form (or array form): 50=5x₂ - 7x₂ 4x₂ + 7x₂ +30=0 x₁ - 7x₂ = 40-3x₂ + 5x₁ Use Python to solve for the unknowns. In addition, compute the transpose and inverse of the coefficient matrix.Simplify the following function using * ?Karnaugh Map F=2%(1,3,57)Define, A = Find x. Hint: use numpy.linalg.inv and the @operator for matrix multiplication. 3 6 2 b = 10 5 2 1 7 5 Y = 3 Ax + b = Y N
