In the following code snippet, can the inner loop be parallelized? Can the outer loop be parallelized? Give reasons. Assume matrix a is already initialized and has appropriate dimension for the code snippet to work. for (i = 1; i < n; ++i) { for (10; j

In the following code snippet, can the inner loop be parallelized? Can the outer loop be parallelized? Give reasons. Assume matrix a is already initialized and has appropriate dimension for the code snippet to work. for (i = 1; i < n; ++i) { for (10; j

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

See similar textbooks

Similar questions

Create an ABM function that takes the following parameters: n := number of paths to be simulated m := number of discretization points per path S0 := initial starting point dS=μdt+σdW Program the function by using two nested "for loops" def ABM(n,m,S0,mu,sigma,dt): np.random.seed(999) arr = # create 2D zeros array with the correct dimensions arr[,] = #initialize column 0 # fill in array entries for i in : for j in : arr[i,j] = return arr
Correct and detailed answer will be Upvoted In C Question: How can the function, matrix_initialize_unopt, be optimized and rewritten with loop collapsing? n has a size of 3000 struct fn_args { int n; int *mem1; int *mem2; }; int check(int x, int y) { return x < y; } void set(int *mat, int i, int num) { mat[i] = num; } void matrix_initialize_unopt(struct fn_args *args) { int i, j, n; int *mat1, *mat2; n = args->n; mat1 = args->mem1; mat2 = args->mem2; for (i = 0; check(i, n); i++){ for (j = 0; check(j, n); j++){ set(mat1, i * n + j, i); set(mat2, i * n + j, i+1); } } }
State the PSEUDOCODE and the loop/recursion invariant alongwith the runtime of the algorithm that you would write for the below question:- You are given data about a friendship network. For every pair of friends, you are given the number of times they have exchanged messages in the last year. Give an efficient algorithm for partitioning the people in the network into k clusters, or communities in such a way that the minimum number of messages exchanged by any two members in different communities is maximized.
Imagine a collection of nuts and bolts that are all together in one pile on a table. Describe, in pseudocode, how you would find all matching pairs of nuts and bolts. You need to find one solution for each of the problem-solving approaches given below. For each of your solution, determine how many comparisons of pairs of nuts and bolts you might have to make in the best- and worst- case scenario. You can assume that there are complete pairs, no single nuts or bolts, and that for each bolt, there is exactly one nut that fits. Describe a solution to the nuts and bolts problem (in pseudocode) using a Divide and Conquer Approach.
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.
A matrix is an array of numbers of size m by n (i.e., m x n). When we multiply 2 matrices, we multiply the matching numbers, then sum them up. Multiplying a matrix of size m x n with another matrix of size n x q will result in a matrix of size m x q. An example is shown in Figure 1: [ ? ? ? ? ? ? ] x [ ? ? ? ? ] = [ ?? +?? ?? + ?? ?? + ?? ?? + ?? ?? + ?? ?? + ?? ] Matrix 3 x 2 Matrix 2 x 2 Matrix 3 x 2 Figure 1: Multiplication of Matrix 3 x 2 and 2 x 2 Write a C++ program that multiplies 2 matrices and return the sum of the transposed matrix. Your program shall include the following: a) Define two 2 dimensional arrays, M and N that each represents a matrix of size 3 x 2 and a matrix of size 2 x 2. Request the user to enter the values for the 2 arrays using advanced pointer notations (refer to your slides for the list of array pointer notations). b) Create a function named multMatrix() that passes as arguments, the values of array M and N, multiplies them and store the results in a new…
Consider the following for loops in R. For each for loop, list the values (in order) that the variable i takes on in the body of the loop. Briefly (in no more than a few sentences) explain why. a) for(i in 1+2:3.4*5) { } b) for(i in dim(matrix(0, nr = 7, nc = 8))) { } c) for(i in rnorm(3)) { } d) for(i in iris[1:3,3]) { } e) for(j in c(1, 2, 3, 4, 5)) { } f) for(i in (function(x) x*x)(c(1, 2, 3))) { } g) for(i in NULL) { } h) for(i in strsplit(as.character(4*atan(1)),’’) [[1]][1:10]) { }
1(a). For an M/M/1 queuing system, write your own function in R to compute andoutput the:1. long-run probability po,p1,…,pn of there being 0,..,n people in the system.2. the values L, Lq, W, Wq, and Wse. The function should return elements with the names probs (vector of po,…,pn), L, Lq, W,Wq, and Wse (does not have to be in this order) The function should take as inputs, lambda (arrival rate), mu (service rate), and n (thenumber of people to compute probabilities for 0,…,n) Note: you are to code the computations for the above on your own, so none of the functionsin the queueing package are to be used. 1(b). Using your function from 1(a), find the values (b)-(f) in the Queuing Theory(M_M_1).
Suppose that your implementation of a particular algorithm appears in C++ as for (int pass = 1; pass <= n; pass++) { for (int index = 0; index < n; index++) { for (int count = 1; count < 10; count++) { ... } // end for } // end for } // end for The previous code shows only the repetition in the algorithm, not the computations that occur within the loops. These computations, however, are independent of n. What is the Big O of the algorithm?
1 Implement a function performing gradient descent using numerical solution (STOCHASTIC gradient descent) def myGradientDescentFun(X,y,learning_rate,epoches, batchsize): # Inputs: Training data, training label, learrning rate, number of epoches, batch size # Output: the final Weights # the loss history along batches
The drawback of this approach, is that with each recursive function call, we only reduce the size of the array by one element, and we use a storage space for one activation record. A faster approach is, with each recursive call, reduce the size of the array by 3 elements. a. Write a reduce-and-conquer recursive function FastRecSum()(C++ or python) that find the sum of all float (real) elements in an array of size n. b. Using addition of float (real) values as a basic operation, set up and solve a recurrence relationship to compute the complexity of your code, as a function of the size n of the array.
Please answer this question- Implement Floyd's algorithm for the Shortest Path on your system, and study its performance using different graphs. Side note- Below I have attached Floyd's Algorithm for Shortest Paths. please use java language for coding java file - should start with your Last Name, Like SmithClubApp, PetrowskiTravelLogApp, for your class short paragraph should include- -description of the java file -what it does? -what is the input data? expected output, samples of both