You have m dollars and a group of n friends. For each friend 1 ≤ i ≤n, you know the price P[i] of the piece of candy that would make your friend happy. You want to find a way to distribute the m dollars such that as many of your friends as possible are happy. Design an O(n log n) time greedy algorithm to find how much money you will allocate each friend.
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You have m dollars and a group of n friends. For each friend 1 ≤ i ≤n, you know the price P[i] of the piece of candy that would make your friend happy. You want to find a way to distribute the m dollars such that as many of your friends as possible are happy. Design an O(n log n) time greedy
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- We are given three ropes with lengths n₁, n2, and n3. Our goal is to find the smallest value k such that we can fully cover the three ropes with smaller ropes of lengths 1,2,3,...,k (one rope from each length). For example, as the figure below shows, when n₁ = 5, n₂ 7, and n3 = 9, it is possible to cover all three ropes with smaller ropes of lengths 1, 2, 3, 4, 5, 6, that is, the output should be k = 6. = Devise a dynamic-programming solution that receives the three values of n₁, n2, and n3 and outputs k. It suffices to show Steps 1 and 2 in the DP paradigm in your solution. In Step 1, you must specify the subproblems, and how the value of the optimal solutions for smaller subproblems can be used to describe those of large subproblems. In Step 2, you must write down a recursive formula for the minimum number of operations to reconfigure. Hint: You may assume the value of k is guessed as kg, and solve the decision problem that asks whether ropes of lengths n₁, n2, n3 can be covered by…You are going to purchase items from a store that can carry a maximal weight of 'w' into your knapsack. There are 5 items in store available and each items weight are Wi and the worth of these items are Pi dollars. What items should you take and how using Knapsack algorithm?The weight of knapsack depend upon the sum of your two digits of age for example suppose your age is 25 then sum of age becomes 7. The list of items and their respective weight withprice are given in table. Items Weight Price A Total count of your first name Total count of your first namedivide by 2 B 3 Your roll no mod 5 C Second digit of your roll no 5 D Total count of your last name Total count of your first namedivide by 2 E Your roll no mod 3 S Sum of roll no mod 13We are to complete a set of n jobs using m identical machines. Each job has a known duration. The goal is to schedule the jobs on m machines so that the time to finish the last job is as small as possible. Once a job is scheduled on a machine, it can’t be stopped and restarted. Any job can run on any machine. A greedy algorithm to solve this problem is as follows: sort jobs in decreasing order of duration. Schedule jobs one by one, choosing the machine where it can start the earliest. a. For m = 3 and n = 6 where the durations are <9, 12, 3, 8, 6, 5>, what is the finish time of the last job using greedy schedule? b. Exhibit an input for which the greedy schedule is NOT optimal.
- Assuming you possess a total of 'm' dollars, and are accompanied by a group of 'n' friends. For every friend i, where i ranges from 1 to n, the price P[i] of the candy that would bring contentment to the respective friend is known. The objective is to devise a method for allocating a sum of m dollars in a manner that maximizes the number of contented friends. Propose an O(n log n) time greedy algorithm for determining the monetary allocation to be assigned to each friend.There are n students who studied at a late-night study for final exam. The time has come to order pizzas. Each student has his own list of required toppings (e.g. mushroom, pepperoni, onions, garlic, sausage, etc). Everyone wants to eat at least half a pizza, and the topping of that pizza must be in his reqired list. A pizza may have only one topping. How to compute the minimum number of pizzas to order to make everyone happy?Roses 1 2 3 4 5 Profit $5 $15 $24 $30 $35 For each positive integer n, let f(n) be the maximum profit that Flora can make with n roses.For example, if n = 10, Flora can make numerous bouquet combinations, including two 5-rose bouquets (total profit of $70), and a 4-rose bouquet with three 2-rose bouquets (total profit of $75). Provide two different algorithms for calculating f(n): one using Recursion, and one using Dynamic Programming. Explain why both algorithms are guaranteed to return the correct value of f(n).
- Let pn(x) be the probability of selling the house to the highest bidder when there are n people, and you adopt the Look-Then-Leap algorithm by rejecting the first x people. For all positive integers x and n with x < n, the probability is equal to p(n(x))= x/n (1/x + 1/(x+1) + 1/(x+2) + … + 1/(n-1)) If n = 100, use the formula above to determine the integer x that maximizes the probability n = 100 that p100(x). For this optimal value of x, calculate the probability p100(x). Briefly discuss the significance of this result, explaining why the Optimal Stopping algorithm produces a result whose probability is far more than 1/n = 1/100 = 1%.Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Design a bottom-up (non-recursive) O(nk)-time algorithm that makes change for any set of k different coin denominations. Write down the pseudocode and analyze its running time. Argue why your choice of the array and the order in which you ll in the values is the correct one.Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Prove that the coin changing problem exhibits optimal substructure. Design a recursive backtracking (brute-force) algorithm that returns the minimum number of coins needed to make change for n cents for any set of k different coin denominations. Write down the pseudocode and prove that your algorithm is correct.
- Consider the problem of making change for n cents using the fewest number of coins. Assume that we live in a country where coins come in k dierent denominations c1, c2, . . . , ck, such that the coin values are positive integers, k ≥ 1, and c1 = 1, i.e., there are pennies, so there is a solution for every value of n. For example, in case of the US coins, k = 4, c1 = 1, c2 = 5, c3 = 10, c4 = 25, i.e., there are pennies, nickels, dimes, and quarters. To give optimal change in the US for n cents, it is sufficient to pick as many quarters as possible, then as many dimes as possible, then as many nickels as possible, and nally give the rest in pennies. Design a bottom-up (non-recursive) O(nk)-time algorithm that makes change for any set of k different coin denominations. Write down the pseudocode and analyze its running time. Argue why your choice of the array and the order in which you fill in the values is the correct one. Notice how it is a lot easier to analyze the running time of…Let's say there are n villages, {X1, . . . , Xn} on the country-road and we aim to build K < n restaurants to cover them. Each restaurant has to be built in a village, and we hope to minimize the average distance from each village to the closest restaurant. Please give an algorithm to compute the optimal way to place these K restaurants. The algorithm should run in O(k * n^2) time. Solutions with slightly higher time complexity also accepted.An electrician has wired n lights, all initially on, so that: 1) light 1 can always be turned on/off, and 2) for k > 1, light k cannot be turned either on or off unless light k – 1 is on and all preceding lights are off for k > 1. The question we want to explore is the following: how many moves are required to turn all n lights off? For n = 5, a solution sequence has been worked out below. Fill in the missing entries. The lights are counted from left to right, so the first bit is the first light, and so on. 11111 01111 11011 10011 00010 10010 11010