ling can escape local optima. 2. Simulated Annealing with a constant and positive temperature at all times is the same as Hill-Climbing search. 3. Simulated Annealing with a linearly decreasing temperature is guaranteed to converge to a globally optimal solution after a finite number of iterat

Recall the rod cutting problem we discussed in class and in the book. While it might be possible for a theoretical person to cut the rod of length n, up to n times to achieve the maximum possible sale value, enterprising children do not possess the stamina to cut the rod so many times. They instead get tired or bored after only k cuts. Design a dynamic programming algorithm to maximize the value of selling cut rods if at most we can cut the rod k times. Please use elements in the table P to calculate the value of a continuous rod, e.g. an uncut rod of length i has value pi. Further you should analyze: 1. The time complexity of a baseline solution that does not use dynamic programming or memoization that exhaustively explores all potential solutions 2. The time complexity of your dynamic programming solution 3. Describe how you could change your algorithm to so to reconstruct the actual cuts in order achieve the highest value for the rod pieces.

Consider the two versions of Quicksort that are based on the following: (i) Median selection algorithm using the linear-time select method; (ii) The randomized pivot selection algorithm   Which of the two algorithms has a faster worst-case asymptotic behavior? Which of the two algorithms has a faster average-case performance? Provide empirical evidence of your answer by writing the corresponding programs for each and conducting experimental evaluation. Your experimental results should provide evidence of your answer. For this you need to run an adequate number of experiments on a variety of array/list sizes (use large randomly generated lists of integers).

Alert: Don't submit AI generated answer. Alert: Don't submit hand written solution strictly.

Calculate the optimal value of the decision parameter p in the Bresenham's circle drawing algorithm. The stepwise procedure for implementing Bresenham's algorithm for circle drawing is delineated.

Simulated Annealing True or False? 1. Simulated Annealing can escape local optima. 2. Simulated Annealing with a constant and positive temperature at all times is the same as Hill- Climbing search. 3. Simulated Annealing with a linearly decreasing temperature is guaranteed to converge to a globally optimal solution after a finite number of iterations.

8. A school is creating class schedules for its students. The students submit their requested courses and then a program will be designed to find the optimal schedule for all students. The school has determined that finding the absolute best schedule cannot be solved in a reasonablo time. Instead they have decided to use a simpler algorithm that produces a good but non-optimal schedule in a more reasonable amount of time. Which principle does this decision best demonstrate? O A Unreasonable algorithms may sometimes also be undecidable O B. Heuristics can be used to solve some problems for which no reasonable algorithm exsts O C. efficiency Two algorithms that solve the same problem must also have the same O B. Approximate solutions are often identical to optimal solutions

Question 15  pap Suppose we have a Monte Carlo algorithm, A, and a deterministic algorithm, B, for testing if the output of A is correct. How can we use A and B to construct a Las Vegas algorithm? Also, if A succeeds with probability ½ and both A and B run O(n) time, what is the expected running time of the Las Vegas algorithm that is produced?      Full explain this question and text typing work only     We should answer our question within 2 hours takes more time then we will reduce Rating Dont ignore this line

A school is creating class schedules for its students. The students submit their requested courses and then a program will be designed to find the optimal schedule for all students. The school has determined that finding the absolute best schedule cannot be solved in a reasonable time. Instead they have decided to use a simpler algorithm that produces a good but non-optimal schedule in a more reasonable amount of time. Which principle does this decision best demonstrate?