Yahoo Web Search

Search results

1. Top results related to divide & conquer strategy

   • Divide and conquer strategy python

     Top Answer

     Answered Apr 26, 2020 · 1 votes

     This is my python version code for the algorithm in its "closest larger right" version. Obviously, as you can see it is recursive. Recursion is really elegant but a bit tricky because few lines of code condense lots of concepts regarding to algorithms design and the own language they are coded. In my opinion 4 relevant moments are happening:

     • 1) Recursive calls. Function is call to itself. During this step the list progresible slice into halves. Once the atomic unit is reached the base algorithm will be executed over them firstly (step 3). if not solution is reached greater list sizes will be involved in the calculation in further recursions.
     • 2) Termination condition. Previous step is not run forever, it allows stop recursion and going to the next step (base algorithm). Now the code has len(arr) > 1: that means that the atomic unit will be pairs of numbers (or one of three in case of odd list). You can increase the number so that the recursive function will stay less time slicing the list and summarizing the results, but the counterpart is that in a parallelized environment "workers" will have to digest a bigger list.
     • 3) Base algorithm. It makes the essential calculation. Whatever the size of the list, it returns the indexes of its elements to the right closest larger number
     • 4) "Calculation saving". The base algorithm no need to calculated indexes on those numbers resolved in previous recursions. There is also a break to stop calculations once the number gets the index in the current recursion list.

     Other algorithms models could be design, more efficient for sure. It occurs to me ones based on dictionaries or on different slicing strategies.

     def closest_larger_right(arr):- len_total = len(arr) result = [None] * len_total- def recursive(arr, len_total, position=0):- # 2) Termination condition if len(arr) > 1:- mid = len(arr) // 2 left = arr[:mid] right = arr[mid:]- position_left = 0 + position position_right = len(left) + position- # 1) Recursive calls recursive(left, len_total, position_left) recursive(right, len_total, position_right)- # 3) Base algorithm for i in range(len(arr)-1): # 4) Calculation saving if result[i + position] is None: for j in range(i+1, len(arr), 1): if (arr[i] < arr[j]): result[i + position] = j + position break return result- return recursive(arr, len_total)-# output: [2, 2, 3, 7, 5, 7, 7, None]print(closest_larger_right([5, 2, 6, 8, 1, 4, 3, 9]))--

     Check all answers on Stack Overflow

     1/5

   • Divide and conquer strategy to merge multiple sorted arrays

     Top Answer

     Answered Aug 26, 2015 · 4 votes

     A divide and conquer approach would be to have a recursive function k-way-merge(), that gets a list of lists as input, and at each step:

     • If you currently have 1 array, return it (stop clause)
     • Otherwise, split the list of lists to two, and for each half - recursively invoke k-way-merge(). merge the two resulting lists.

     The main aspect you need to change in your code is in:

     int[] first_Half = new int[half]; int[] second_Half = new int[lists.length - half]; -

     Here, you need first_half and second_half to be int[][], as they are actually list of lists.

     In addition, in the last line:

     return merge(merge(first_half),merge(second_half));-

     Note that the outer merge() is different, it's not a recursive call - but a call to "regular" merge(), that merges two arrays into one (as the logic on how to do it is missing from the code, I assume you have such merge() implemented).

     Other than that, the approach looks correct, but a bit inefficient, since you copy the int[][] object every iteration, rather than using indices to "remember where you are".

     Check all answers on Stack Overflow

     2/5

   • Divide and Conquer algorithm in C++

     Top Answer

     Answered Jan 26, 2017 · 0 votes

     Got accepted (finally) using two binary search (each for lower bound, and upper bound) with this code:

     #include<bits/stdc++.h>using namespace std;-long long n,m,i,l,r,mid1,mid2,ans,tmp,cnt,haha,k;long long ar[26214400];long long func(long long x){ haha = 0; for (k = 0; k < n; k++) haha += x/ar[k]; return haha;}int main(){ cin >> n >> m; for(i = 0; i < n; i++) cin >> ar[i]; sort(ar,ar+n); l = ar[0]; r = ar[0]*m; mid1 = (l+r)/2; tmp = func(mid1); while (l < r){ mid1 = (l+r)/2; tmp = func(mid1); if (tmp < m) l = mid1+1; else if (tmp > m) r = mid1-1; else r = mid1-1; } mid1 = l; //lower bound l = ar[0]; r = ar[0]*m; mid2 = (l+r)/2; tmp = func(mid2); while (l < r){ mid2 = (l+r)/2; tmp = func(mid2); if (tmp < m) l = mid2+1; else if (tmp > m) r = mid2-1; else l = mid2+1; } mid2 = r; //upper bound while (mid1 <= mid2 and func(mid1) != m) mid1 += 1; while (mid2 >= mid1 and func(mid2) != m) mid2 -= 1; ans = mid2-mid1+1; cout << ans << endl;}

     Check all answers on Stack Overflow

     3/5

   • Divide and Conquer Matrix Multiplication

     Top Answer

     Answered Jan 31, 2011 · 5 votes

     You are recursively calling divideAndConquer in the wrong way. What your function does is square a matrix. In order for divide and conquer matrix multiplication to work, it needs to be able to multiply two potentially different matrixes together.

     It should look something like this:

     private static int[][] divideAndConquer(int[][] matrixA, int[][] matrixB){ if (matrixA.length == 2){ //calculate and return base case } else { //make a11, b11, a12, b12 etc. by dividing a and b into quarters int[][] c11 = addMatrix(divideAndConquer(a11,b11),divideAndConquer(a12,b21)); int[][] c12 = addMatrix(divideAndConquer(a11,b12),divideAndConquer(a12,b22)); int[][] c21 = addMatrix(divideAndConquer(a21,b11),divideAndConquer(a22,b21)); int[][] c22 = addMatrix(divideAndConquer(a21,b12),divideAndConquer(a22,b22)); //combine result quarters into one result matrix and return }}

     Check all answers on Stack Overflow

     4/5

   • Divide-and-conquer algorithms' property example

     Top Answer

     Answered Jan 30, 2014 · 2 votes

     You're not missing anything. The theorem and its proof are wrong. The error is here:

     T(n) = T(k) + T(n-k) + 1-

     The constant term of 1 should be 2, as the function makes one recursive call for each of the two pieces into which it divides the problem. The correct bound is 2N-1, rather than N. Hopefully, this error will be fixed in the next edition of your textbook, or at least in the errata.

     Check all answers on Stack Overflow

     5/5

   Show moreShow less

2. www.geeksforgeeks.org › divide-and-conquerDivide and Conquer Algorithm - GeeksforGeeks

   www.geeksforgeeks.org › divide-and-conquer

   • Cached

   Apr 30, 2024 · Divide and Conquer algorithm is a problem-solving strategy that involves breaking down a complex problem into smaller, more manageable parts, solving each part individually, and then combining the solutions to solve the original problem. It is a widely used algorithmic technique in computer science and mathematics.

3. en.wikipedia.org › wiki › Divide-and-conquer_algorithmDivide-and-conquer algorithm - Wikipedia

   en.wikipedia.org › wiki › Divide-and-conquer_algorithm

   • Cached

   A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem. The divide-and-conquer technique is the basis of efficient algorithms ...

4. People also ask

   What is a divide and conquer strategy?

   • “Divide” is the first step of the divide and conquer strategy. As suggested by the name, in this step we divide the problem into smaller subproblems until the problem is small enough to be solved. At this step, sub-problems become smaller but still represent some part of the actual problem.

   Divide and Conquer Algorithms | Baeldung on Computer Science

   www.baeldung.com/cs/divide-and-conquer-strategy

   See all results for this question

   What is a divide and conquer algorithm?

   • In computer science, divide and conquer is an algorithm design paradigm. A divide-and-conquer algorithm recursively breaks down a problem into two or more sub-problems of the same or related type, until these become simple enough to be solved directly. The solutions to the sub-problems are then combined to give a solution to the original problem.

   Divide-and-conquer algorithm - Wikipedia

   en.wikipedia.org/wiki/Divide-and-conquer_algorithm

   See all results for this question

   How do you divide and conquer a problem?

   • Divide and conquer can be done in three broad steps, divide (into subproblems), conquer (by solving the subproblems), and combine (the answers to solve the original problem). Divide and conquer has a recursive step, where subproblems are solved, and a base case, which is the point where the problem can't be broken down any further.

   Divide and Conquer | Brilliant Math & Science Wiki

   brilliant.org/wiki/divide-and-conquer/

   See all results for this question

   What is a divide and conquer paradigm?

   • The divide-and-conquer paradigm is often used to find an optimal solution of a problem. Its basic idea is to decompose a given problem into two or more similar, but simpler, subproblems, to solve them in turn, and to compose their solutions to solve the given problem. Problems of sufficient simplicity are solved directly.

   Divide-and-conquer algorithm - Wikipedia

   en.wikipedia.org/wiki/Divide-and-conquer_algorithm

   See all results for this question

5. www.programiz.com › dsa › divide-and-conquerDivide and Conquer Algorithm - Programiz

   www.programiz.com › dsa › divide-and-conquer

   • Cached

   A divide and conquer algorithm is a strategy of solving a large problem by. breaking the problem into smaller sub-problems. solving the sub-problems, and. combining them to get the desired output. To use the divide and conquer algorithm, recursion is used. Learn about recursion in different programming languages:

6. www.baeldung.com › cs › divide-and-conquer-strategyDivide and Conquer Algorithms | Baeldung on Computer Science

   www.baeldung.com › cs › divide-and-conquer-strategy

   • Cached

   Mar 18, 2024 · Steps for Divide and Conquer Algorithms. 2.1. Divide. “Divide” is the first step of the divide and conquer strategy. As suggested by the name, in this step we divide the problem into smaller subproblems until the problem is small enough to be solved. At this step, sub-problems become smaller but still represent some part of the actual problem.

7. Videos

   • 

     7:04

     youtube.com

     2 Divide And Conquer

     Jan 22, 2018

     990.5K Views

   • 

     9:46

     youtube.com

     Divide and conquer strategy || general method || merge sort with an example and algorithm || DAA

     Mar 1, 2022

     19.2K Views

   • 

     4:03

     youtube.com

     L-3.0: Divide and Conquer | Algorithm

     Mar 12, 2021

     613.9K Views

   View all

8. www.freecodecamp.org › news › divide-and-conquerDivide and Conquer Algorithm Meaning: Explained with Examples

   www.freecodecamp.org › news › divide-and-conquer

   • Cached

   Nov 26, 2019 · The Divide and Conquer algorithm solves the problem in O(nLogn) time. Strassen’s Algorithm is an efficient algorithm to multiply two matrices. A simple method to multiply two matrices need 3 nested loops and is O(n^3). Strassen’s algorithm multiplies two matrices in O(n^2.8974) time. Cooley–Tukey Fast Fourier Transform (FFT) algorithm is ...

9. www.geeksforgeeks.org › introduction-to-divide-andIntroduction to Divide and Conquer Algorithm - GeeksforGeeks

   www.geeksforgeeks.org › introduction-to-divide-and

   • Cached

   May 1, 2024 · Divide and Conquer Algorithm is a problem-solving technique used to solve problems by dividing the main problem into subproblems, solving them individually and then merging them to find solution to the original problem. In this article, we are going to discuss how Divide and Conquer Algorithm is helpful and how we can use it to solve problems.

10. brilliant.org › wiki › divide-and-conquerDivide and Conquer | Brilliant Math & Science Wiki

    brilliant.org › wiki › divide-and-conquer

    • Cached

    Divide and conquer is a way to break complex problems into smaller problems that are easier to solve, and then combine the answers to solve the original problem. Divide and conquer is a powerful algorithm design technique used to solve many important problems such as mergesort, quicksort, calculating Fibonacci numbers, and performing matrix multiplication. There are also many problems that ...