Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 27.1, Problem 6E
Program Plan Intro
The following is multithreaded
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Let us consider multiplying a 5 by 5 sparse matrix with a 5 by 3 sparse matrix shown in picture.
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The Burrows-Wheeler transform (BWT) is a transformation that is used in data compression algorithms, including bzip2, and in high-throughput sequencing in genomics. Write a SuffixArray client that computes the BWT in linear time, as follows: Given a string of length N (terminated by a special end-of-file character $ that is smaller than any other character), consider the N-by-N matrix in which each row contains a different cyclic rotation of the original text string. Sort the rows lexicographically. The Burrows-Wheeler transform is the rightmost column in the sorted matrix. For example, the BWT of Mississippi is ipssm$pissii. The Burrows-Wheeler inverse transform (BWI) inverts the BWT. For example, the BWI of ipssm$pissii is mississippi$. Also write a client that, given the BWT of a text string, computes the BWI in linear time.
Let C be a linear [n, k]-code with alphabet GF(q). Let C+ be any code obtained from C by lengthening, that is, first adding one extra bit to every codeword of C and then adding one extra row to a generating matrix for C while maintaining linear independence.
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Chapter 27 Solutions
Introduction to Algorithms
Ch. 27.1 - Prob. 1ECh. 27.1 - Prob. 2ECh. 27.1 - Prob. 3ECh. 27.1 - Prob. 4ECh. 27.1 - Prob. 5ECh. 27.1 - Prob. 6ECh. 27.1 - Prob. 7ECh. 27.1 - Prob. 8ECh. 27.1 - Prob. 9ECh. 27.2 - Prob. 1E
Ch. 27.2 - Prob. 2ECh. 27.2 - Prob. 3ECh. 27.2 - Prob. 4ECh. 27.2 - Prob. 5ECh. 27.2 - Prob. 6ECh. 27.3 - Prob. 1ECh. 27.3 - Prob. 2ECh. 27.3 - Prob. 3ECh. 27.3 - Prob. 4ECh. 27.3 - Prob. 5ECh. 27.3 - Prob. 6ECh. 27 - Prob. 1PCh. 27 - Prob. 2PCh. 27 - Prob. 3PCh. 27 - Prob. 4PCh. 27 - Prob. 5PCh. 27 - Prob. 6P
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- (c) Consider an n x n array ARR stored in memory consisting of 0’s and 1’s such that, in a row of ARR, all 0’s comes before any of 1’s in the row. Write an algorithm having complexity O(n), if exists, that finds the row that contains the most 0’s. Step by step explain your algorithm with an illustrative example.arrow_forward## Count the number of unique paths from a[0][0] to a[m-1][n-1]# We are allowed to move either right or down from a cell in the matrix.# Approaches-# (i) Recursion- Recurse starting from a[m-1][n-1], upwards and leftwards,# add the path count of both recursions and return count.# (ii) Dynamic Programming- Start from a[0][0].Store the count in a count# matrix. Return count[m-1][n-1]# T(n)- O(mn), S(n)- O(mn)# def count_paths(m, n): if m < 1 or n < 1: return -1 count = [[None for j in range(n)] for i in range(m)] # Taking care of the edge cases- matrix of size 1xn or mx1 for i in range(n): count[0][i] = 1 for j in range(m): count[j][0] = 1 for i in range(1, m): for j in range(1, n): # Number of ways to reach a[i][j] = number of ways to reach # a[i-1][j] + a[i][j-1] count[i][j] = count[i - 1][j] + count[i][j - 1]…arrow_forwardConsider the following multithreaded algorithm for performing pairwise multiplication on n-element arrays A[1..n] and B[1..n], storing the multiplications in C[1..n]:Analyze the work, span and parallelism of this algorithm.arrow_forward
- One of the methods used to keep graphy in memory is the use of two-array. In this method, connection information is stored in one of the directories, and the index values of the nodes in the other. a) Show the retention of a costly graphene on a two-array by taking the following neighborhood matrix as an example. b) Report the matrices in C language by giving the initial values as well NOT:It should be in C Language, Not Another Languagearrow_forwardConsider the problem of finding the smallest and largest elements in an array of n numbers. For solving this problem, design, respectively, a brute-force algorithm, a divide-and-conquer algorithm, and a presorting-based (transform-and-conquer) algorithm. determine and compare the efficiency in Big-O notation of the above three algorithms.arrow_forwardDescribe an EREW PRAM algorithm to find an item in an array of length n in O(log n) time. If the item appears more than once in the array, then the algorithm should return the lowest index of the location.arrow_forward
- ) Consider an n x n array ARR stored in memory consisting of 0’s and 1’s such that, in a row of ARR, all 0’s comes before any of 1’s in the row. Write an algorithm having complexity O(n), if exists, that finds the row that contains the most 0’s. Step by step explain r algorithm with an illustrative example. 6arrow_forwardillustrates the procedure for Fibonacci search. Here n, thenumber of data elements is such that: i) Fk+1 > (n+1); andii) Fk + m = (n +1) for some m ≥ 0, where Fk+1 and Fk are two consecutiveFibonacci numbers.arrow_forwardGiven 2 sorted arrays (in increasing order), find a path through the intersection that produces the maximum sum and return the maximum sum. That is, we can switch from one array to another array only atcommon elements. If no intersection element is present, we need to take the sum of all elements from the array with greater sum. Sample Input:61 5 10 15 20 2552 4 5 9 15Sample Output :81arrow_forward
- 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.arrow_forwardGiven a matrix of size N x M where N is the number of rows and M is the number of columns, write an algorithm to find the shortest path from the top-left cell to the bottom-right cell that passes through all the cells with a prime value and avoids cells with composite values. The algorithm should have a time complexity of O(NM log(max(N,M))).arrow_forwardSearch a sorted matrix: The input consists of a real number x and a matrix A[1..n, 1..m] of nm real values, where A[i, 1..m] and A[1..n, j] are the rows and columns, respectively, of the matrix. The objective is to either state that all of the members of A are greater than x or to locate the largest array entry A[i, j] that is less than or equal to x. Create and evaluate an iterative approach for this issue that looks at the least amount of matrix entries feasible. If you think the issue can be resolved with a straightforward binary search, think twice.arrow_forward
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