30. Consider the following algorithm: j = 1; while (j <= n/2) { i = 1; while (i <= j) { cout « j << i; i++; } j++; } (a) What is the output when n = 6, n = 8, and n = 10? (b) What is the time complexity T(n)? You may assume that the input n is divisible by 2.
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- Assume that you were given N cents (N is an integer) and you were asked to break up the N cents into coins consisting of 1 cent, 2 cents and 5 cents. Write a dynamicprogramming based recursive algorithm, which returns the smallest (optimal) number of coins needed to solve this problem. For example, if your algorithm is called A, and N = 13, then A(N) = A(13) returns 4, since 5+5+2+1 = 13 used the smallest (optimal) number of coins. In contrast, 5+5+1+1+1 is not an optimal answer.Check these two codes and then answer these questions please: 1) Empirically, show the performance curve of the algorithm using time measurements 2) Using the basics of the theoretical analysis, write the complexity of its worst-casetime. 1. Approach: Non recursive: //include necessary header files#include <iostream>using namespace std;//main functionint main(){ int days,buy_on_this_day ,sell_on_this_day; //get number of days as input from user cout<<"Enter number of days: "; cin>>days; int stock_price[days]; for(int i=0;i<days;i++) { cout<<"Enter stock_price"; cin>>stock_price[i]; } int i=0; for(int i=0;i<days-1;i++) { //comparing current price with next day price and finding the minima while(i<days-1 && stock_price[i+1]<=stock_price[i]) i++; if(i==days-1) break; buy_on_this_day =i++; while(i<days && stock_price[i]>=…Construct a memoized algorithm for this algorithm and and derive its time complexity: Assume that you were given N cents (N is an integer) and you were asked to break up the N cents into coins consisting of 1 cent, 2 cents and 5 cents. Write a dynamicprogramming based recursive algorithm, which returns the smallest (optimal) number of coins needed to solve this problem. For example, if your algorithm is called A, and N = 13, then A(N) = A(13) returns 4, since 5+5+2+1 = 13 used the smallest (optimal) number of coins. In contrast, 5+5+1+1+1 is not an optimal answer. You do not need to prove the complexity bound formally, just derive it by analyzing each component in your algorithm.
- Consider the following algorithm: int f(n) /* n is a positive integer */ if (n <=3) return n int sum = f(n-1) if (n is even) return sum + f(n-2) else return sum + f(n-3) Develop a dynamic programming algorithm that calculates and returns the same value of f(n) for any given integer n>=1. That is, develop a dynamic programming algorithm equivalent to the recursive algorithm f(n). Describe your algorithm using C++ syntax.1. a.An algorithm can be specified in various ways. Identify and explain four ways in which an algorithm can be specified. b. (b). Two algorithms A, B sort the same problem. When you go through each algorithm and break them down into their primitive operations, each can be represented as follows:A = 2n7 + 100n4 + 26n + 50 B = 7n4 + 22n2 + nlogn + 200For very large values of n, which of the algorithms A or B will runin the shortest time to solve the problem and why? (c). Two Computer Science students, Priscilla and Julius, are discussing how to compare two algorithms for solving a given problem. Priscilla suggests that they should use the execution times of the algorithms as criterion; but Julius insists that they should use the number of statements the algorithms execute as criterion. (i). Discuss the reasons why both criteria they are considering are not good for comparing algorithms.(ii). Recommend an ideal solution/criterion that they should rather use for comparing algorithmsLet two words be given. At least how many steps must be taken to make one of these two words like the other? Each step can be one of the following 3 steps: 1- Drop a letter 2- Changing a letter 3- Add a letter (anywhere) Solve the question using only a brute force algorithm. Explain your solution and question write program in java language and calculate time complexity?
- Assume that you were given N cents (N is an integer) and you were asked to break up the N cents into coins consisting of 1 cent, 2 cents and 5 cents. Write a dynamic programming-based recursive algorithm, which returns the smallest (optimal) number of coins needed to solve this problem. For example, if your algorithm is called A, and N = 13, then A(N) = A(13) returns 4, since 5+5+2+1 = 13 used the smallest (optimal) number of coins. In contrast, 5+5+1+1+1 is not an optimal answer. Draw the recursion tree for the algorithm where N = 7. Derive the complexity bound of the algorithm. Do not need to prove the complexity bound formally, just derive it by analyzing each component in your algorithm.Simplistic Algorithms with a Fixed Number of Integers (with C++) Write a program that takes three integers and outputs them in reverse order of how they were entered. For example, if the user enters 39, 51, 16 then the program should output 16, 51, 39.Artificial Intelligence - Local Search Starting from a randomly generated state of the 15-puzzle game (https://en.wikipedia.org/wiki/15_puzzle), steepest-ascent hill-climbing (the vanilla version of hill-climbing search) gets stuck 76% of the time, i.e., solving only 24% of problem instances. But it works very quickly, i.e., it takes just 6 steps on average when it succeeds and 5 steps when it gets stuck. In contrast, if sideways moves are allowed, this raises the percentage of problem instances solved by hill-climbing from 24% to 81%, with the success at a cost: the algorithm averages roughly 7 steps for each successful instance and 32 steps for each failure. Now suppose that we are implementing random-restart hill climbing (i.e., if a search fails, it keeps to try, and try, until it gets a success) by the following two versions: one uses vanilla steepest-ascent hill climbing, and the other one uses hill climbing with sideways moves. Can you please tell which version of…
- Discuss another algorithm under Divide and Conquer. In your discussion, include the following: 1. Introduction about the algorithm. 2. The pseudocode 3. Explanation of its complexityAssume that each of the following expressions indicates the number of operations performed by an algorithm for a problem size of n. Point out the dominant term of each algorithm and use big-O notation to classify it. 2n – 4n2 + 5n 3n2 + 6 n3 + n2 – n1. Analyze the following code using step account (total number of steps). Give the detailed analysis. What is the asymptotic complexity of the above algorithm?(a) int s = 0;(b) for( int i = 1; i < n; i + +)(c) for (int j = 1; j <= i; j + +)(d) s = s + j;