Assume you have a 2D matrix. Values are sorted in ascending order from left to write and top to bottom, e.g., 1 7 16 21 34 4 9 20 25 41 6. 16 31 33 44 11 19 49 50 61 I'm curious if my value x is in this matrix. I could take a brute force approach, considering all cells via the following code: def find in matrix(m, num rows, num cols, x): for į in range(num rows): for j in range(num cols): if m[i]li)==x: return True return False What would be the big O estimate of the run-time for this brute force approach? How could I take a divide-and-conquer approach? Briefly describe (in words) how you could use what is known about the structure of the 2D matrix to divide and conquer.

Please use python if applicable and comment on code, thank you

Find a sorted matrix: The input is a real number x and a matrix A[1..n, 1..m] of nm real numbers, with each row A[i, 1..m] and column A[1..n, j] sorted. The objective is to locate the largest array entry A[i, j] that is less than or equal to x, or to report that all components of A are greater than x. Create and test an iterative method that analyses as few matrix elements as feasible. Be cautious if you assume a simple binary search would fix the problem. Find a sorted matrix: The input is a real number x and a matrix A[1..n, 1..m] of nm real numbers, with each row A[i, 1..m] and column A[1..n, j] sorted. The objective is to locate the largest array entry A[i, j] that is less than or equal to x, or to report that all components of A are greater than x. Create and test an iterative method that analyses as few matrix elements as feasible. Be cautious if you assume a simple binary search would fix the problem.

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