APCSP_SemReview_Algorithms

Consider the following multiplication problem: Given an integer list (an array) of size n, we want to calculate the product of all numbers in the list and display the result. (a) Define an instance of a problem in general. (b) Specify two different instances of the multiplication problem defined above. (c) What is the solution for each instance in part (b). How did you come up with that solution?

In computer science and mathematics, the Josephus Problem (or Josephus permutation) is a theoretical problem. Following is the problem statement: There are n people standing in a circle waiting to be executed. The counting out begins at some point (rear) in the circle and proceeds around the circle in a fixed direction. In each step, a certain number (k) of people are skipped and the next person is executed. The elimination proceeds around the circle (which is becoming smaller and smaller as the executed people are removed), until only the last person remains, who is given freedom. Given the total number of persons n and a number k which indicates that k-1 persons are skipped and kth person is killed in circle. The task is to choose the place in the initial circle so that you are the last one remaining and so survive. For example, if n = 5 and k = 2, then the safe position is 3. Firstly, the person at position 2 is killed, then person at position 4 is killed, then person at position 1…

Problem 1. Construct a non-recursive procedure capable of reversing a single linked list of n elements, which runs in O(n) time. Can the same be achieved in (n) time? If so, construct it.

The Longest Subsequence Problem is a well-studied problem in Computer Science, where given a sequence of distinct positive integers, the goal is to output the longest subsequence whose elements appear from smallest to largest, or from largest to smallest. For example, consider the sequence S = [9,7,4,10,6,8,2,1,3,5]. The longest increasing subsequence of S has length three ([4,6,8] or [2,3,5]), and the longest decreasing subsequence of S has length five([9,7,4,2,1] or [9,7,6,2,1]). And if we have the sequence S = [531,339,298,247,246,195,104,73,52,31], then the length of the longest increasing subsequence is 1 and the length of the longest decreasing subsequence is 10. Question: Let S be a sequence with ten distinct integers. Prove by Contradiction that there must exist an increasing subsequence of length 4 (or more) or a decreasing subsequence of length 4 (or more). Hint: for each integer k in the sequence you found in the first part, define the ordered pair (x(k), y(k)), where x(k)…

Sudoku is a popular logic puzzle that uses a 9 by 9 array of squares that are organized into 3 by 3 subarrays. The puzzle solver must fill in the squares with the digits 1 to 9 such that no digit is repeated in any row, any column, or any of the nine 3 by 3 subgroups of squares. Initially, some squares are filled in already and cannot be changed. For example, the following might be a starting configuration for a Sudoku puzzle: Create a class SudokuPuzzle.java Download SudokuPuzzle.java that has the attributes • board—a 9 by 9 array of integers that represents the current state of the puzzle, where 0 indicates a blank square • start—a 9 by 9 array of boolean values that indicates which squares in board are given values that cannot be changed and the following methods: • SudokuPuzzle—a constructor that creates an empty puzzle • toString—returns a string representation of the puzzle that can be printed • addInitial(row, col, value)—sets the given square to the given value as an…

Algorithm design with sorting. Each of n users spends some time on a social media site. For each i = 1, . . . , n, user i enters the site at time ai and leaves at time bi ≥ ai. You are interested in the question: how many distinct pairs of users are ever on the site at the same time? (Here, the pair (i, j) is the same as the pair (j, i)).Example: Suppose there are 5 users with the following entering and leaving times: Then, the number of distinct pairs of users who are on the site at the same time is five: these pairs are (1, 2), (1, 3), (2, 3), (4, 6), (5, 6). (Drawing the intervals on a number line may make this easier to see).(a) Given input (a1 , b1),(a2 , b2), . . . ,(an, bn) as above in no particular order (i.e., not sorted in any way), describe a straightforward algorithm that takes Θ(n2)-time to compute the number of pairs of users who are ever on the site at the same time, and explain why it takes Θ(n2)-time. [We are expecting pseudocode and a brief justification for its…

Merge sort is an efficient sorting algorithm with a time complexity of O(n log n). This means that as the number of elements (chocolates or students) increases significantly, the efficiency of merge sort remains relatively stable compared to other sorting algorithms. Merge sort achieves this efficiency by recursively dividing the input array into smaller sub-arrays, sorting them individually, and then merging them back together. The efficiency of merge sort is primarily determined by its time complexity, which is , where n is the number of elements in the array. This time complexity indicates that the time taken by merge sort grows logarithmically with the size of the input array. Therefore, even as the number of chocolates or students increases significantly, merge sort maintains its relatively efficient performance. Regarding the distribution of a given set of x to y using iterative and recursive functions, the complexity analysis depends on the specific implementation of each…

The ColumnChoice problem takes as input a two-dimensional array A[1..m, 1..n] of Os and 1s with m rows and n columns along with a non-negative integer k < n. It asks whether there exists a subset SC {1, ..., n} of k columns such that for each row i e {1,..., m} there exists at least one column j e S where A[i, j] = 1. Prove that ColumnChoice is NP- complete.

Exercise 3. Given the following recurrence relation: T(1) = 1       T(n) = T(n − 1) + n2 for n ≥ 2 Indicate a methodology you can use to evaluate a closed form expression Provide the closed form expression and show the procedure to obtain it Provide a recursive algorithm in pseudo code to calculate a generic T(n) Provide an analysis of the algorithm above

A certain recursive algorithm takes an input list of n elements. Divides the list into Vn sub-lists, each with yn elements. Recursively solves each of these yn smaller sub- instances. Then spends an additional 0(n) time to combine the solutions of these sub- instances to obtain the solution of the main instance. As a base case, if the size of the input list is at most a specified positive constant, then the algorithm solves such a small instance directly in 0(1) time. a) Express the recurrence relation that governs T(n), the time complexity of this algorithm. b) Derive the solution to this recurrence relation: T(n) = 0(?). Mention which methods you used to derive your solution.

1. Analyze the word finder with wild card characters scenario to form the algorithm and compute the time complexity. You are given a list of characters with size n, m. You need to read character data provided as input into this list. There is also a list of p words that you need to find in the given problem. You can only search in left, right, up and down direction from a position. There are two wild characters *' and '+' ,if there is any instance of wild card characters for example '+' appears you can match whatever character in your word that you are searching at this position (only one character). Similarly, if you got wildcard *' you can skip any number of characters ( A sequence of characters not necessary same in between the characters) but the last character should be matched in the given data. This means you are not allowed to match a first and last character with *' wildeard. Input: The first line of the input will give you two integers n and m. From the next line you will get…

A magic square of order n is an arrangement of the integers from 1 to n2 in an n × n matrix, with each number occurring exactly once, so that each row, each column, and each main diagonal has the same sum Design and implement an exhaustive-search algorithm for generating all magic squares of order n.

PLEASE USE PYTHONGiven a jungle matrix NxM:jungle = [ [1, 0, 0, 0], [1, 1, 0, 1], [0, 1, 0, 0], [1, 1, 1, 1,]]Where 0 means the block is dead end and 1 means the block can be used in the path fromsource to destination.Task:Starting at position (0, 0), the goal is to reach position (N-1, M-1).Your program needs to build and output the solution matrix – a 4x4 matrix with 1’s inpositions used to get from the starting position (0,0) to the ending position (N-1,M-1)with the following constraints:You can only move one space at a timeYou can only in two directions: forward and down.You can only pass thru spaces on the jungle matrix marked ‘1’If you cannot reach the ending position – print a message that you’re trapped in thejungleAlgorithm:If destination is reachedprint the solution matrixElseMark current cell in the solution matrixMove forward horizontally and recursively check if this leads to a solution If there is no solution, move down and recursively check if this leads to a solution If…

We consider an improvement for the linear search by organizing the list based upon frequency of access. What is not one of the three often used heuristics involving maintaining this frequency as discussed in our lecture? move to front mark and replace transpose order by count all of these none of these

develop an implementation TwoSumFaster that uses a linear algorithm to count the pairs that sum to zero after the array is sorted (instead of the binary-search-based linearithmic algorithm). Then apply a similar idea to develop a quadratic algorithm for the 3-sum problem.

Question 2: The game of "FastestPath" consists of a path (a 1-D array) with n positive integers to represent n cities in a path (except the first index which has always value 0). The aim of the game is to move from the source city (located at index 0) to destination (located at last index) in the shortest time. The value at each index shows the travel time to enter the city located in the corresponding index. Here is a sample path where there are 6 cities (n is 6): |0 5 |90 | 7 61 | 12 Always start the game from the first city and there are two types of moves. You can either move to the adjacent city or jump over the adjacent city to land two cities over. The total travel time of a game is the sum of the travel times of the visited cities. In the path shown above, there are several ways to get to the end. Starting in the first city, our time so far is 0. We could travel to city 2, then travel to city 4, then travel to last city for a total travel time of 90 + 61 + 12 = 163. However, a…

Please do not give solution in image formate thanku.  Write a Python code for the following scenario :   1: Use Breadth First Search and Recursive Best First Search and implement the algorithms in Python. The program should be able to dynamically take in start and goal nodes from the user at run time. Compare to interpret the results in terms of the algorithm working, performance & shortest path if obtained relevant to the given problem 2: Print the minimum connections that keep the whole network up and running. For each incremental depth limit, the corresponding node generating sequence should be legibly printed in the result   Consider Heuristic Value as : The edge cost is an approximation towards the transmission cost between any pair of nodes. For heuristic design, consider all the possible paths between any arbitrary node n to the goal node. The average of the total transmission cost across all these paths is the heuristic value h(n)   Consider the following undirected graph…

The intersection of two sets contains the list of elements that are common to both, without repetition. As such, you are required to write an algorithm that finds and returns the intersection of two sets A and B. The sets A and B and the resulting intersection set are all lists. Assume the size of A is n and that of B is m. (a) Write an iterative algorithm that finds the intersection. (b) Prove the correctness of your algorithm; i.e. state its loop invariant and prove it by induc- tion. (c) Analyze the time complexity of your algorithm. (d) Think of a way to enhance the complexity of your algorithm by sorting both lists A and B before calculating their intersection. Write the enhanced version and prove that it has a better time complexity.

1. Write an algorithm to determine whether a given element x belongs to a set S := {s1, . . . , sn}.

Devise an algorithm that matches the lower bound, i.e., sorts a list in which each paper is within k slots of its proper position in e (n log k) time. Hint: See Exercise 6.5-8 on page 166 of Introduction to Algorithms, Third Edition.

4. Consider the function IndexEqual(A,i.j) that returns true if there exists an index x (i sx sj) such that A[x] = x; otherwise, retums false. You may assume A is a sorted integer array in which every element is unique. a. Write an efficient recursive algorithm for IndexEqual(A,i.j). b. What is the situation resulting in the best-case running time of your function, and give an expression for that running time? c. What is the situation resulting in the worst-case running time of your function, and give an expression for that running time in terms of n, where n=j-i+1?

Assuming that quick sort algorithm presented selects the first element in the list as the pivot. (A)  Give a list of n items (for example, an array of 10 integers) representing a scenario. (B)  Give a list of n items (for example, an array of 10 integers) representing the worst-case scenario. (C)  Give a list of n items (for example, an array of 10 integers) representing the best-case scenario.

Question 6: Professor Holmes has come up with a new sorting algorithm. He calls it Trinary Sort and "claims" that it is asymptotically faster than Merge Sort, despite the fact both the algorithms operate using similar logic. But, unlike Merge Sort, Trinary Sort splits the input array into (roughly) 3 equal parts at each step of the recursion as long as the array is splittable (i.e., has at least 3 elements). Trinary Sort's merge subroutine, similar in principle to the one used by Merge Sort, takes 3 sorted subarrays and merges them to produce a single sorted array. Given all of this, answer the following questions. (a) In Merge Sort, the merge subroutine makes n-1 comparisons to merge 2 arrays of size n/2, which takes (n) time. How many comparisons will the merge subroutine of Trinary Sort make to merge 3 arrays of size n/3? What would be the (...) bound on the running time for this subroutine? (b) What is the (...) bound on the running time of the Trinary Sort algorithm? Come up with…

Computer Science Write the PSEUDOCODE for an algorithm that takes as input a list of numbers that are sorted in nondecreasing order, and finds the location(s) of the most frequently occurring element(s) in the list. If there are more than one element that is the most frequently occurring, then return the locations of all of them. Analyze the worst-case time complexity of this algorithm and give the O() estimate. (A list is in nondecreasing order if each number in the list is greater than or equal to the number preceding it.)

In python, The Longest Subsequence Problem is a well-studied problem in Computer Science, where given a sequence of distinct positive integers, the goal is to output the longest subsequence whose elements appear from smallest to largest, or from largest to smallest. For example, consider the sequence S= [9,7,4,10,6,8,2,1,3,5]. The longest increasing subsequence of S has length three ([4,6,8] or [2,3,5]), and the longest decreasing subsequence of S has length five([9,7,4,2,1] or [9,7,6,2,1]). And if we have the sequence S = [531,339,298,247,246,195,104,73,52,31], then the length of the longest increasing subsequence is 1 and the length of the longest decreasing subsequence is 10. Question: Find a sequence with nine distinct integers for which the length of the longest increasing subsequence is 3, and the length of the longest decreasing subsequence is 3. Briefly explain how youconstructed your sequence.

Design an algorithm to find min and max-The algorithm should do, at most, about 3n/2 comparisons (in the worst case scenario) for problems of size n elements.- The algorithm is supposed to take in a list, assumed to be integers.- The list given to the algorithm is unsorted.

To add the two polynomials, we assume that the singly linked lists' nodes are organized in decreasing order of the variable x's exponents.The goal is to generate a new list of nodes reflecting the sum of P1 and P2. This is accomplished by combining the COEFF fields of nodes with comparable powers of variable x in lists P1 and P2, and then adding a new node in the resultant list P1 + P2. The procedure's core is presented below.P1 and P2 are the beginning pointers of the singly linked lists that symbolize polynomials P1 and P2. Furthermore, PTR1 and PTR2 are two temporary pointers that are originally set to P1 and P2, respectively. Procedure code should be written.

Design a reduction algorithm that solves the below problem;                                                              Describe your algorithm with clear pseudocode, and mention the time and space efficiency class of your algorithm in terms of the size of the input Also, identify the pre/post-processing steps Problem: input: a list L of comparable objectsoutput: an element of L that appears more than once in L, or None if no such element exists