Q1: MCM Algorithm ImplementationImplement Matrix Chain Multiplication (MCM) algorithm using the following pseudocode asinstructed in the Class Lecture.MATRIX-CHAIN-ORDER (p)1 n length[p] - 12 for i +1 to ndo m[i, i] +04 for / +2 ton3Dl is the chain length.do for i +1 ton-1+1do j+i+1-1m[i, j] + 00for k +i to j - 156.789.do q + m[i, k] + m[k + 1, j]+ Pi–1 PkPjif q < m[i, j]then m[i, j] +qs[i, j] +k10111213 return m and s

Problem Statement: Implementation of Matrix Chain Multiplication algorithm. Since there is no…

Q1: MCM Algorithm Implementation Implement Matrix Chain Multiplication (MCM) algorithm using the following pseudocode as instructed in the Class Lecture. MATRIX-CHAIN-ORDER (p) 1 ne length[p] - 1 2 for i +1 ton 3 do m[i, i] +0 4 for / +2 to n 5 Dl is the chain length. do for i +1 to n-1+1 do jei+l-1 mli, j] + 0 for k +i to j - 1 7 8 9. do q + m[i, k] + m[k + 1, j]+ Pi–1 PkPj if q < m[i, j] then m[i, j] + q s[i, j] +k 10 11 12 13 return m and s

Q1: MCM Algorithm Implementation Implement Matrix Chain Multiplication (MCM) algorithm using the following pseudocode as instructed in the Class Lecture. MATRIX-CHAIN-ORDER (p) 1 ne length[p] - 1 2 for i +1 ton 3 do m[i, i] +0 4 for / +2 to n 5 Dl is the chain length. do for i +1 to n-1+1 do jei+l-1 mli, j] + 0 for k +i to j - 1 7 8 9. do q + m[i, k] + m[k + 1, j]+ Pi–1 PkPj if q < m[i, j] then m[i, j] + q s[i, j] +k 10 11 12 13 return m and s

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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#plea# 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]…
Computer Science Given an N x N matrix M with binary entries i.e every entry is either 1 or 0. You are told that every row and every column is sorted in increasing order. You are required to output a pair (i,j) with 1 <= i and j <= n corresponding to the entry of the matrix satisfying Mij = 1 and Mrs = 0 for all 1 <= r <= i and 1 <= s <= j except for Mij Informally this includes the entry of M = 1 and is closest to the top left corner. for example: M = [ 0 0 0 1 0 0 1 1 0 0 1 1 0 0 1 1] output is (2,3) or (1,4) M = [ 0 1 1 1 1 1 1 1 1] output could be (1,2) or (2,1) Design a divide and conquer algorithm, explain correctness and runtime of the algorithm.
#com# 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]…
Matrix multiplication plays an important role in a number of applications. Two matrices can only be multiplied if the number of columns of the fi rst matrix is equal to the number of rows in the second.Let’s assume we have an m × n matrix A and we want to multiply it by an n × p matrix B. We can express their product as an m × p matrix denoted by AB (or A ⋅ B). If we assign C = AB, and ci,j denotes the entry in C at position (i, j), then for each element i and j with 1 ≤ i ≤ m and 1 ≤ j ≤ p. Now we want to see if we can parallelize the computation of C. Assume that matrices are laid out in memory sequentially as follows: a1,1, a2,1, a3,1, a4,1, ..., etc.Assume that we are going to compute C on both a single core shared memory machine and a 4-core shared-memory machine. Compute the speedup we would expect to obtain on the 4-core machine, ignoring any memory issues.Repeat above Exercise, assuming that updates to C incur a cache miss due to false sharing when consecutive elements are in a…
Search 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.
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Search a sorted matrix: The input consistsof a real number x and a matrix A[1..n, 1..m] of nm real numbers such that each rowA[i, 1..m] is sorted and each column A[1..n, j] is sorted. The goal is to find the maximum array entry A[i, j] that is less than or equal to x, or report that all elements of Aare larger than x. Design and analyze an iterative algorithm for this problem that examines as few matrix entries as possible. Becareful if you believe that a simple binarysearch solves the problem. make a lower bound, and recursive algorithm.
Search a sorted matrix: The input consistsof a real number x and a matrix A[1..n, 1..m] of nm real numbers such that each rowA[i, 1..m] is sorted and each column A[1..n, j] is sorted. The goal is to find the maximum array entry A[i, j] that is less than or equal to x, or report that all elements of Aare larger than x. Design and analyze an iterative algorithm for this problem that examines as few matrix entries as possible. Becareful if you believe that a simple binarysearch solves the problem.
## 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]…
Given an n x n matrix where each of the rows and columns is sorted in ascending order, return the kth smallest element in the matrix. Note that it is the kth smallest element in the sorted order, not the kth distinct element. You must find a solution with a memory complexity better than O(n2). Example 1: Input: matrix = [[1,5,9],[10,11,13],[12,13,15]], k = 8 Output: 13 Explanation: The elements in the matrix are [1,5,9,10,11,12,13,13,15], and the 8th smallest number is 13 Example 2: Input: matrix = [[-5]], k = 1 Output: -5
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4) Given that a queue is being represented by the following circular representation in an array (front is currently 4 and back is currently 7): array component: 0 1 2 4 5 6 7 8 | | | | 37 | 41 | 12 | 34 | | a) Show what this representation would like after the following sequence of operations are performed: deq(q, i); deq(q, j); enq(q, 81); enq(q, 72); enq(q, 38); deq(q, k); (indicate the current position of front and back for each operation) b) What is an advantage of this implementation of a queue? c) What is a disadvantage of this implementation of a queue?