Write Algorithm for Hertel–Mehlhorn method for convex partition. in: polygon P out: convex partition R

= Trace the Winnow algorithm with 3: 1 for the following input. Suppose the domain is vectors of length n = 6 over {V1, V2, V3, V4, V5, V6} and the actual labels are with respect to formula v₁/v3/v6; for example, the true label of (1, 0, 1, 0, 1, 1) is '1' and that of 1, 1, 1, 1, 1,0 is '0'. The input vectors are: x1 = (1, 0, 1, 1, 0, 1) x2 = (1,0,1,1,0,0) x3 = x4= X5 x6 x7 x8 = = = = x9 = (0, 1, 0, 1, 0, 1) 1, 1, 1, 0, 0, 1) (1, (0, 1, 1, 1, 1,0) (1, 1, 1, 1, 1, 1) (0, 1, 0, 1, 1, 1) (1, 1, 0, 1, 1, 1) (1, 0, 1, 0, 0, 1) You need to follow the same assumptions as in the example in Slide 4 of Module 11. Show your work and specify whether it is possible that the algorithm makes more mistakes after processing the above vectors; justify your answer.

Write a recurrence for this algorithm and solve it to obtain a tight upper bound on the worst case runtime of this algorithm. You can use any method you like for solving this recurrence.

6. Numpy.linalg https://numpy.org/doc/stable/reference/generated/numpy.linalg. solve.html. Generate a random, square matrix and random vector using numpy and then solve using numpy.linalg.solve(). Confirm it is a solution using 'A.dot (x)' where x is the solution from solve(). import numpy as np n=100 Numpy has its own black-box linear system solver numpy.linalg.solve(): A=np.random.rand (n,n) b=np.random.rand (n,1) I