. An implicit finite difference scheme solves for an n x 1 vector x using an...
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. An implicit finite difference scheme solves for an n x 1 vector x using an n x n coefficient matrix A and an n x 1 force vector b. The governing equation is Ax = b and can be solved in MATLAB using x A\b. Here, we want to test the computational complexity for x =A\b in a specific scheme where A and b are set up as follows: A(i, j) = 0 except: o o o o O A(i, j) = -1 ifj=i-1 or j=i+1 A (i, j) = 2 ifj equals i A(1,1)= 1 A(n,n) = 1 Also, b(i) = 0 except b(1) = b(n) equals 1. a. Determine theoretically how to determine the computational complexity exponent p based on two points (n₁, t₁) and (n₂, t₂). b. Plot the time required using 50 ≤ n ≤ 100 for x A\b [hint: initialize A and b arrays using A zeros (n) and b = zeros (n,1), and then overwrite specific values of A and b]. Pick two points that avoid outliers and estimate the computational complexity. . An implicit finite difference scheme solves for an n x 1 vector x using an n x n coefficient matrix A and an n x 1 force vector b. The governing equation is Ax = b and can be solved in MATLAB using x A\b. Here, we want to test the computational complexity for x =A\b in a specific scheme where A and b are set up as follows: A(i, j) = 0 except: o o o o O A(i, j) = -1 ifj=i-1 or j=i+1 A (i, j) = 2 ifj equals i A(1,1)= 1 A(n,n) = 1 Also, b(i) = 0 except b(1) = b(n) equals 1. a. Determine theoretically how to determine the computational complexity exponent p based on two points (n₁, t₁) and (n₂, t₂). b. Plot the time required using 50 ≤ n ≤ 100 for x A\b [hint: initialize A and b arrays using A zeros (n) and b = zeros (n,1), and then overwrite specific values of A and b]. Pick two points that avoid outliers and estimate the computational complexity.
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