Skip to content
geeksforgeeks
  • Tutorials
    • Python
    • Java
    • Data Structures & Algorithms
    • ML & Data Science
    • Interview Corner
    • Programming Languages
    • Web Development
    • CS Subjects
    • DevOps And Linux
    • School Learning
    • Practice Coding Problems
  • Courses
    • DSA to Development
    • Get IBM Certification
    • Newly Launched!
      • Master Django Framework
      • Become AWS Certified
    • For Working Professionals
      • Interview 101: DSA & System Design
      • Data Science Training Program
      • JAVA Backend Development (Live)
      • DevOps Engineering (LIVE)
      • Data Structures & Algorithms in Python
    • For Students
      • Placement Preparation Course
      • Data Science (Live)
      • Data Structure & Algorithm-Self Paced (C++/JAVA)
      • Master Competitive Programming (Live)
      • Full Stack Development with React & Node JS (Live)
    • Full Stack Development
    • Data Science Program
    • All Courses
  • DSA Tutorial
  • Data Structures
  • Algorithms
  • Array
  • Strings
  • Linked List
  • Stack
  • Queue
  • Tree
  • Graph
  • Searching
  • Sorting
  • Recursion
  • Dynamic Programming
  • Binary Tree
  • Binary Search Tree
  • Heap
  • Hashing
  • Divide & Conquer
  • Mathematical
  • Geometric
  • Bitwise
  • Greedy
  • Backtracking
  • Branch and Bound
  • Matrix
  • Pattern Searching
  • Randomized
Open In App
Next Article:
Coin Change - Count Ways to Make Sum
Next article icon

Sum over Subsets | Dynamic Programming

Last Updated : 29 Mar, 2024
Comments
Improve
Suggest changes
Like Article
Like
Report

Prerequisite: Basic Dynamic Programming, Bitmasks 
Consider the following problem where we will use Sum over subset Dynamic Programming to solve it. 
Given an array of 2n integers, we need to calculate function F(x) = ?Ai such that x&i==i for all x. i.e, i is a bitwise subset of x. i will be a bitwise subset of mask x, if x&i==i.
Examples: 
 

Input: A[] = {7, 12, 14, 16}  ,  n = 2  Output: 7, 19, 21, 49  Explanation: There will be 4 values of x: 0,1,2,3  So, we need to calculate F(0),F(1),F(2),F(3).  Now, F(0) = A0 = 7   F(1) =  A0 + A1 = 19  F(2) = A0 + A2 = 21  F(3) = A0 + A1 + A2 + A3 = 49    Input: A[] = {7, 11, 13, 16}  ,  n = 2  Output: 7, 18, 20, 47   Explanation: There will be 4 values of x: 0,1,2,3  So, we need to calculate F(0),F(1),F(2),F(3).  Now, F(0) = A0 = 7   F(1) =  A0 + A1 = 18  F(2) = A0 + A2 = 20  F(3) = A0 + A1 + A2 + A3 = 47


 


Brute-Force Approach: 
Iterate for all the x from 0 to (2n-1) . Calculate the bitwise subsets of all the x and sum it up for every x.
Time-Complexity: O(4^n)
Below is the implementation of above idea:
 

C++
// CPP program for brute force // approach of SumOverSubsets DP #include <bits/stdc++.h> using namespace std;  // function to print the sum over subsets value void SumOverSubsets(int a[], int n) {    // array to store the SumOverSubsets   int sos[1 << n] = {0};    // iterate for all possible x   for (int x = 0; x < (1 << n); x++) {      // iterate for all possible bitwise subsets     for (int i = 0; i < (1 << n); i++) {        // if i is a bitwise subset of x       if ((x & i) == i)         sos[x] += a[i];     }   }    // printa all the subsets   for (int i = 0; i < (1 << n); i++)     cout << sos[i] << " "; }  // Driver Code int main() {   int a[] = {7, 12, 14, 16};   int n = 2;   SumOverSubsets(a, n);   return 0; } 
Java
// Java program for brute force // approach of SumOverSubsets DP  class GFG{  // function to print the // sum over subsets value static void SumOverSubsets(int a[], int n) {  // array to store the SumOverSubsets int sos[] = new int [1 << n];   // iterate for all possible x for (int x = 0; x < (1 << n); x++) {      // iterate for all possible         // bitwise subsets     for (int i = 0; i < (1 << n); i++) {      // if i is a bitwise subset of x     if ((x & i) == i)         sos[x] += a[i];     } }  // printa all the subsets for (int i = 0; i < (1 << n); i++)     System.out.printf("%d ", sos[i]); }  // Driver Code public static void main(String[] args) { int a[] = {7, 12, 14, 16}; int n = 2; SumOverSubsets(a, n); } }  // This code is contributed by  // Smitha Dinesh Semwal 
Python3
# Python 3 program # for brute force # approach of SumOverSubsets DP  # function to print the # sum over subsets value def SumOverSubsets(a, n):      # array to store     # the SumOverSubsets     sos = [0] * (1 << n)          # iterate for all possible x     for x in range(0,(1 << n)):               # iterate for all         # possible bitwise subsets         for i in range(0,(1 << n)):                    # if i is a bitwise subset of x             if ((x & i) == i):                 sos[x] += a[i]                            # printa all the subsets     for i in range(0,(1 << n)):          print(sos[i],end = " ")   # Driver Code a = [7, 12, 14, 16] n = 2 SumOverSubsets(a, n)  # This code is contributed by # Smitha Dinesh Semwal 
C#
// C# program for brute force // approach of SumOverSubsets DP using System;  class GFG {          // function to print the     // sum over subsets value     static void SumOverSubsets(int []a, int n)     {              // array to store the SumOverSubsets         int []sos = new int [1 << n];                           // iterate for all possible x         for (int x = 0; x < (1 << n); x++)          {                      // iterate for all possible             // bitwise subsets             for (int i = 0; i < (1 << n); i++)              {                          // if i is a bitwise subset of x                 if ((x & i) == i)                     sos[x] += a[i];             }         }                  // printa all the subsets         for (int i = 0; i < (1 << n); i++)             Console.Write(sos[i] + " ");     }          // Driver function     public static void Main()     {         int []a = {7, 12, 14, 16};         int n = 2;         SumOverSubsets(a, n);     } }  // This code is contributed by Sam007 
PHP
<?php // PHP program for brute force // approach of SumOverSubsets DP  // function to print the sum  // over subsets value function SumOverSubsets($a, $n) {      // array to store the SumOverSubsets     $sos = array(1 << $n);          for($i = 0 ;$i < (1 << $n); $i++)         $sos[$i] = 0;           // iterate for all possible x     for ($x = 0; $x < (1 << $n); $x++)     {              // iterate for all possible          // bitwise subsets         for($i = 0; $i < (1 << $n); $i++)         {                  // if i is a bitwise             // subset of x             if (($x & $i) == $i)                 $sos[$x] += $a[$i];         }     }          // printa all the subsets     for ($i = 0; $i < (1 << $n); $i++)         echo $sos[$i] . " "; }  // Driver Code $a = array(7, 12, 14, 16); $n = 2; SumOverSubsets($a, $n);  // This code is contributed by Sam007 ?> 
JavaScript
<script>     // Javascript program for brute force     // approach of SumOverSubsets DP          // function to print the     // sum over subsets value     function SumOverSubsets(a, n)     {               // array to store the SumOverSubsets         let sos = new Array(1 << n);         sos.fill(0);                             // iterate for all possible x         for (let x = 0; x < (1 << n); x++)         {                       // iterate for all possible             // bitwise subsets             for (let i = 0; i < (1 << n); i++)             {                           // if i is a bitwise subset of x                 if ((x & i) == i)                     sos[x] += a[i];             }         }                   // printa all the subsets         for (let i = 0; i < (1 << n); i++)             document.write(sos[i] + " ");     }          let a = [7, 12, 14, 16];     let n = 2;     SumOverSubsets(a, n);          </script> 

Output: 
 

7 19 21 49 

. 
Sub-Optimal Approach: 
The brute-force algorithm can be easily improved by just iterating over bitwise subsets. Instead of iterating for every i, we can simply iterate for the bitwise subsets only. Iterating backward for i=(i-1)&x gives us every bitwise subset, where i starts from x and ends at 1. If the mask x has k set bits, we do 2k iterations. A number of k set bits will have 2k bitwise subsets. Therefore total number of mask x with k set bits is{n \choose k}          . Therefore the total number of iterations is ?{n \choose k}          2k = 3n 
Time Complexity: O(3n)
Below is the implementation of above idea: 
 

C++
// CPP program for sub-optimal // approach of SumOverSubsets DP #include <bits/stdc++.h> using namespace std;  // function to print the sum over subsets value void SumOverSubsets(int a[], int n) {    // array to store the SumOverSubsets   int sos[1 << n] = {0};    // iterate for all possible x   for (int x = 0; x < (1 << n); x++) {     sos[x] = a[0];      // iterate for the bitwise subsets only     for (int i = x; i > 0; i = (i - 1) & x)       sos[x] += a[i];   }    // print all the subsets   for (int i = 0; i < (1 << n); i++)     cout << sos[i] << " "; }  // Driver Code int main() {   int a[] = {7, 12, 14, 16};   int n = 2;   SumOverSubsets(a, n);   return 0; } 
Java
// java program for sub-optimal // approach of SumOverSubsets DP public class GFG {          // function to print the sum over     // subsets value     static void SumOverSubsets(int a[], int n)     {              // array to store the SumOverSubsets         int sos[] = new int[(1 << n)];                  // iterate for all possible x         for (int x = 0; x < (1 << n); x++) {             sos[x] = a[0];                      // iterate for the bitwise subsets only             for (int i = x; i > 0; i = (i - 1) & x)                 sos[x] += a[i];         }                  // print all the subsets         for (int i = 0; i < (1 << n); i++)             System.out.print(sos[i] + " ");     }          // Driver code     public static void main(String args[])     {         int a[] = {7, 12, 14, 16};         int n = 2;                  SumOverSubsets(a, n);     } }  // This code is contributed by Sam007 
Python3
# Python program for sub-optimal # approach of SumOverSubsets DP  # function to print sum over subsets value def SumOverSubsets(a, n):     sos = [0]*(1 << n)      # iterate for all possible x     for x in range((1 << n)):         sos[x] = a[0]                  # iterate for the bitwise subsets only         i = x          while i > 0:           sos[x] += a[i]           i = ((i - 1) & x)    # print all the subsets     for i in range(1<<n):         print(sos[i], end = " ")  # Driver Code if __name__ == '__main__':     a = [7, 12, 14, 16]     n = 2     SumOverSubsets(a, n)  # This code is contributed by mohit kumar 29. 
C#
// C# program for sub-optimal // approach of SumOverSubsets DP using System;  class GFG {          // function to print the sum over     // subsets value     static void SumOverSubsets(int []a, int n)     {              // array to store the SumOverSubsets         int []sos = new int[(1 << n)];                  // iterate for all possible x         for (int x = 0; x < (1 << n); x++) {             sos[x] = a[0];                      // iterate for the bitwise subsets only             for (int i = x; i > 0; i = (i - 1) & x)                 sos[x] += a[i];         }                  // print all the subsets         for (int i = 0; i < (1 << n); i++)         Console.Write(sos[i] + " ");     }          // Driver code     static void Main()     {         int []a = {7, 12, 14, 16};         int n = 2;                  SumOverSubsets(a, n);     } }  // This code is contributed by Sam007. 
PHP
<?php // PHP program for sub-optimal // approach of SumOverSubsets DP  // function to print the  // sum over subsets value function SumOverSubsets($a,$n)  {      // array to store the SumOverSubsets     $sos=array(1 << $n);          // iterate for all possible x     for ($x = 0; $x < (1 << $n); $x++)      {         $sos[$x] = $a[0];              // iterate for the bitwise         // subsets only         for ($i = $x; $i > 0; $i = ($i - 1) & $x)         $sos[$x] += $a[$i];     }          // print all the subsets     for ($i = 0; $i < (1 << $n); $i++)         echo $sos[$i] . " "; }  // Driver Code $a = array(7, 12, 14, 16); $n = 2; SumOverSubsets($a, $n);  // This code is contributed by Sam007. ?> 
JavaScript
<script>         // Javascript program for sub-optimal     // approach of SumOverSubsets DP          // function to print the sum over     // subsets value     function SumOverSubsets(a, n)     {               // array to store the SumOverSubsets         let sos = new Array((1 << n));         sos.fill(0);                   // iterate for all possible x         for (let x = 0; x < (1 << n); x++) {             sos[x] = a[0];                       // iterate for the bitwise subsets only             for (let i = x; i > 0; i = (i - 1) & x)                 sos[x] += a[i];         }                   // print all the subsets         for (let i = 0; i < (1 << n); i++)             document.write(sos[i] + " ");     }          let a = [7, 12, 14, 16];     let n = 2;      SumOverSubsets(a, n);  </script> 


Output: 
 

7 19 21 49 

Time Complexity: O(n*2n) 

Auxiliary Space: O(2n)

Reference: 


Next Article
Coin Change - Count Ways to Make Sum

S

Striver
Improve
Article Tags :
  • Misc
  • Advanced Data Structure
  • DSA
  • Bit Algorithms
Practice Tags :
  • Advanced Data Structure
  • Misc

Similar Reads

    Dynamic Programming or DP
    Dynamic Programming is an algorithmic technique with the following properties.It is mainly an optimization over plain recursion. Wherever we see a recursive solution that has repeated calls for the same inputs, we can optimize it using Dynamic Programming. The idea is to simply store the results of
    3 min read
    What is Memoization? A Complete Tutorial
    In this tutorial, we will dive into memoization, a powerful optimization technique that can drastically improve the performance of certain algorithms. Memoization helps by storing the results of expensive function calls and reusing them when the same inputs occur again. This avoids redundant calcula
    6 min read
    Dynamic Programming (DP) Introduction
    Dynamic Programming is a commonly used algorithmic technique used to optimize recursive solutions when same subproblems are called again.The core idea behind DP is to store solutions to subproblems so that each is solved only once. To solve DP problems, we first write a recursive solution in a way t
    15+ min read
    Tabulation vs Memoization
    Tabulation and memoization are two techniques used to implement dynamic programming. Both techniques are used when there are overlapping subproblems (the same subproblem is executed multiple times). Below is an overview of two approaches.Memoization:Top-down approachStores the results of function ca
    9 min read
    Optimal Substructure Property in Dynamic Programming | DP-2
    The following are the two main properties of a problem that suggest that the given problem can be solved using Dynamic programming: 1) Overlapping Subproblems 2) Optimal Substructure We have already discussed the Overlapping Subproblem property. Let us discuss the Optimal Substructure property here.
    3 min read
    Overlapping Subproblems Property in Dynamic Programming | DP-1
    Dynamic Programming is an algorithmic paradigm that solves a given complex problem by breaking it into subproblems using recursion and storing the results of subproblems to avoid computing the same results again. Following are the two main properties of a problem that suggests that the given problem
    10 min read
    Steps to solve a Dynamic Programming Problem
    Steps to solve a Dynamic programming problem:Identify if it is a Dynamic programming problem.Decide a state expression with the Least parameters.Formulate state and transition relationship.Apply tabulation or memorization.Step 1: How to classify a problem as a Dynamic Programming Problem? Typically,
    13 min read

    Advanced Topics

    Count Ways To Assign Unique Cap To Every Person
    Given n people and 100 types of caps labelled from 1 to 100, along with a 2D integer array caps where caps[i] represents the list of caps preferred by the i-th person, the task is to determine the number of ways the n people can wear different caps.Example:Input: caps = [[3, 4], [4, 5], [5]] Output:
    15+ min read
    Digit DP | Introduction
    Prerequisite : How to solve a Dynamic Programming Problem ?There are many types of problems that ask to count the number of integers 'x' between two integers say 'a' and 'b' such that x satisfies a specific property that can be related to its digits.So, if we say G(x) tells the number of such intege
    14 min read
    Sum over Subsets | Dynamic Programming
    Prerequisite: Basic Dynamic Programming, Bitmasks Consider the following problem where we will use Sum over subset Dynamic Programming to solve it. Given an array of 2n integers, we need to calculate function F(x) = ?Ai such that x&i==i for all x. i.e, i is a bitwise subset of x. i will be a bit
    10 min read

    Easy problems in Dynamic programming

    Coin Change - Count Ways to Make Sum
    Given an integer array of coins[] of size n representing different types of denominations and an integer sum, the task is to count all combinations of coins to make a given value sum. Note: Assume that you have an infinite supply of each type of coin. Examples: Input: sum = 4, coins[] = [1, 2, 3]Out
    15+ min read
    Subset Sum Problem
    Given an array arr[] of non-negative integers and a value sum, the task is to check if there is a subset of the given array whose sum is equal to the given sum. Examples: Input: arr[] = [3, 34, 4, 12, 5, 2], sum = 9Output: TrueExplanation: There is a subset (4, 5) with sum 9.Input: arr[] = [3, 34, 4
    15+ min read
    Introduction and Dynamic Programming solution to compute nCr%p
    Given three numbers n, r and p, compute value of nCr mod p. Example: Input: n = 10, r = 2, p = 13 Output: 6 Explanation: 10C2 is 45 and 45 % 13 is 6.We strongly recommend that you click here and practice it, before moving on to the solution.METHOD 1: (Using Dynamic Programming) A Simple Solution is
    15+ min read
    Rod Cutting
    Given a rod of length n inches and an array price[]. price[i] denotes the value of a piece of length i. The task is to determine the maximum value obtainable by cutting up the rod and selling the pieces.Note: price[] is 1-indexed array.Input: price[] = [1, 5, 8, 9, 10, 17, 17, 20]Output: 22Explanati
    15+ min read
    Painting Fence Algorithm
    Given a fence with n posts and k colors, the task is to find out the number of ways of painting the fence so that not more than two consecutive posts have the same color.Examples:Input: n = 2, k = 4Output: 16Explanation: We have 4 colors and 2 posts.Ways when both posts have same color: 4 Ways when
    15 min read
    Longest Common Subsequence (LCS)
    Given two strings, s1 and s2, the task is to find the length of the Longest Common Subsequence. If there is no common subsequence, return 0. A subsequence is a string generated from the original string by deleting 0 or more characters, without changing the relative order of the remaining characters.
    15+ min read
    Longest Increasing Subsequence (LIS)
    Given an array arr[] of size n, the task is to find the length of the Longest Increasing Subsequence (LIS) i.e., the longest possible subsequence in which the elements of the subsequence are sorted in increasing order.Examples: Input: arr[] = [3, 10, 2, 1, 20]Output: 3Explanation: The longest increa
    14 min read
    Longest subsequence such that difference between adjacents is one
    Given an array arr[] of size n, the task is to find the longest subsequence such that the absolute difference between adjacent elements is 1.Examples: Input: arr[] = [10, 9, 4, 5, 4, 8, 6]Output: 3Explanation: The three possible subsequences of length 3 are [10, 9, 8], [4, 5, 4], and [4, 5, 6], wher
    15+ min read
    Maximum size square sub-matrix with all 1s
    Given a binary matrix mat of size n * m, the task is to find out the maximum length of a side of a square sub-matrix with all 1s.Example:Input: mat = [ [0, 1, 1, 0, 1], [1, 1, 0, 1, 0], [0, 1, 1, 1, 0], [1, 1, 1, 1, 0], [1, 1, 1, 1, 1], [0, 0, 0, 0, 0] ]Output: 3Explanation: The maximum length of a
    15+ min read
    Min Cost Path
    You are given a 2D matrix cost[][] of dimensions m × n, where each cell represents the cost of traversing through that position. Your goal is to determine the minimum cost required to reach the bottom-right cell (m-1, n-1) starting from the top-left cell (0,0).The total cost of a path is the sum of
    15+ min read
    Longest Common Substring (Space optimized DP solution)
    Given two strings ‘s1‘ and ‘s2‘, find the length of the longest common substring. Example: Input: s1 = “GeeksforGeeks”, s2 = “GeeksQuiz” Output : 5 Explanation:The longest common substring is “Geeks” and is of length 5.Input: s1 = “abcdxyz”, s2 = “xyzabcd” Output : 4Explanation:The longest common su
    7 min read
    Count ways to reach the nth stair using step 1, 2 or 3
    A child is running up a staircase with n steps and can hop either 1 step, 2 steps, or 3 steps at a time. The task is to implement a method to count how many possible ways the child can run up the stairs.Examples: Input: 4Output: 7Explanation: There are seven ways: {1, 1, 1, 1}, {1, 2, 1}, {2, 1, 1},
    15+ min read
    Grid Unique Paths - Count Paths in matrix
    Given an matrix of size m x n, the task is to find the count of all unique possible paths from top left to the bottom right with the constraints that from each cell we can either move only to the right or down.Examples: Input: m = 2, n = 2Output: 2Explanation: There are two paths(0, 0) -> (0, 1)
    15+ min read
    Unique paths in a Grid with Obstacles
    Given a matrix mat[][] of size n * m, where mat[i][j] = 1 indicates an obstacle and mat[i][j] = 0 indicates an empty space. The task is to find the number of unique paths to reach (n-1, m-1) starting from (0, 0). You are allowed to move in the right or downward direction. Note: In the grid, cells ma
    15+ min read

    Medium problems on Dynamic programming

    0/1 Knapsack Problem
    Given n items where each item has some weight and profit associated with it and also given a bag with capacity W, [i.e., the bag can hold at most W weight in it]. The task is to put the items into the bag such that the sum of profits associated with them is the maximum possible. Note: The constraint
    15+ min read
    Printing Items in 0/1 Knapsack
    Given weights and values of n items, put these items in a knapsack of capacity W to get the maximum total value in the knapsack. In other words, given two integer arrays, val[0..n-1] and wt[0..n-1] represent values and weights associated with n items respectively. Also given an integer W which repre
    12 min read
    Unbounded Knapsack (Repetition of items allowed)
    Given a knapsack weight, say capacity and a set of n items with certain value vali and weight wti, The task is to fill the knapsack in such a way that we can get the maximum profit. This is different from the classical Knapsack problem, here we are allowed to use an unlimited number of instances of
    15+ min read
    Egg Dropping Puzzle | DP-11
    You are given n identical eggs and you have access to a k-floored building from 1 to k.There exists a floor f where 0 <= f <= k such that any egg dropped from a floor higher than f will break, and any egg dropped from or below floor f will not break. There are a few rules given below:An egg th
    15+ min read
    Word Break
    Given a string s and y a dictionary of n words dictionary, check if s can be segmented into a sequence of valid words from the dictionary, separated by spaces.Examples:Input: s = "ilike", dictionary[] = ["i", "like", "gfg"]Output: trueExplanation: The string can be segmented as "i like".Input: s = "
    12 min read
    Vertex Cover Problem (Dynamic Programming Solution for Tree)
    A vertex cover of an undirected graph is a subset of its vertices such that for every edge (u, v) of the graph, either ‘u’ or ‘v’ is in vertex cover. Although the name is Vertex Cover, the set covers all edges of the given graph. The problem to find minimum size vertex cover of a graph is NP complet
    15+ min read
    Tile Stacking Problem
    Given integers n (the height of the tower), m (the maximum size of tiles available), and k (the maximum number of times each tile size can be used), the task is to calculate the number of distinct stable towers of height n that can be built. Note:A stable tower consists of exactly n tiles, each stac
    15+ min read
    Box Stacking Problem
    Given three arrays height[], width[], and length[] of size n, where height[i], width[i], and length[i] represent the dimensions of a box. The task is to create a stack of boxes that is as tall as possible, but we can only stack a box on top of another box if the dimensions of the 2-D base of the low
    15+ min read
    Partition a Set into Two Subsets of Equal Sum
    Given an array arr[], the task is to check if it can be partitioned into two parts such that the sum of elements in both parts is the same.Note: Each element is present in either the first subset or the second subset, but not in both.Examples: Input: arr[] = [1, 5, 11, 5]Output: true Explanation: Th
    15+ min read
    Travelling Salesman Problem using Dynamic Programming
    Given a 2d matrix cost[][] of size n where cost[i][j] denotes the cost of moving from city i to city j. The task is to complete a tour from city 0 (0-based index) to all other cities such that we visit each city exactly once and then at the end come back to city 0 at minimum cost.Note the difference
    15 min read
    Longest Palindromic Subsequence (LPS)
    Given a string s, find the length of the Longest Palindromic Subsequence in it. Note: The Longest Palindromic Subsequence (LPS) is the maximum-length subsequence of a given string that is also a Palindrome. Longest Palindromic SubsequenceExamples:Input: s = "bbabcbcab"Output: 7Explanation: Subsequen
    15+ min read
    Longest Common Increasing Subsequence (LCS + LIS)
    Given two arrays, a[] and b[], find the length of the longest common increasing subsequence(LCIS). LCIS refers to a subsequence that is present in both arrays and strictly increases.Prerequisites: LCS, LIS.Examples:Input: a[] = [3, 4, 9, 1], b[] = [5, 3, 8, 9, 10, 2, 1]Output: 2Explanation: The long
    15+ min read
    Find all distinct subset (or subsequence) sums of an array
    Given an array arr[] of size n, the task is to find a distinct sum that can be generated from the subsets of the given sets and return them in increasing order. It is given that the sum of array elements is small.Examples: Input: arr[] = [1, 2]Output: [0, 1, 2, 3]Explanation: Four distinct sums can
    15+ min read
    Weighted Job Scheduling
    Given a 2D array jobs[][] of order n*3, where each element jobs[i] defines start time, end time, and the profit associated with the job. The task is to find the maximum profit you can take such that there are no two jobs with overlapping time ranges.Note: If the job ends at time X, it is allowed to
    15+ min read
    Count Derangements (Permutation such that no element appears in its original position)
    A Derangement is a permutation of n elements, such that no element appears in its original position. For example, a derangement of [0, 1, 2, 3] is [2, 3, 1, 0].Given a number n, find the total number of Derangements of a set of n elements.Examples : Input: n = 2Output: 1Explanation: For two balls [1
    12 min read
    Minimum insertions to form a palindrome
    Given a string s, the task is to find the minimum number of characters to be inserted to convert it to a palindrome.Examples:Input: s = "geeks"Output: 3Explanation: "skgeegks" is a palindromic string, which requires 3 insertions.Input: s= "abcd"Output: 3Explanation: "abcdcba" is a palindromic string
    15+ min read
    Ways to arrange Balls such that adjacent balls are of different types
    There are 'p' balls of type P, 'q' balls of type Q and 'r' balls of type R. Using the balls we want to create a straight line such that no two balls of the same type are adjacent.Examples : Input: p = 1, q = 1, r = 0Output: 2Explanation: There are only two arrangements PQ and QPInput: p = 1, q = 1,
    15+ min read
geeksforgeeks-footer-logo
Corporate & Communications Address:
A-143, 7th Floor, Sovereign Corporate Tower, Sector- 136, Noida, Uttar Pradesh (201305)
Registered Address:
K 061, Tower K, Gulshan Vivante Apartment, Sector 137, Noida, Gautam Buddh Nagar, Uttar Pradesh, 201305
GFG App on Play Store GFG App on App Store
Advertise with us
  • Company
  • About Us
  • Legal
  • Privacy Policy
  • In Media
  • Contact Us
  • Advertise with us
  • GFG Corporate Solution
  • Placement Training Program
  • Languages
  • Python
  • Java
  • C++
  • PHP
  • GoLang
  • SQL
  • R Language
  • Android Tutorial
  • Tutorials Archive
  • DSA
  • Data Structures
  • Algorithms
  • DSA for Beginners
  • Basic DSA Problems
  • DSA Roadmap
  • Top 100 DSA Interview Problems
  • DSA Roadmap by Sandeep Jain
  • All Cheat Sheets
  • Data Science & ML
  • Data Science With Python
  • Data Science For Beginner
  • Machine Learning
  • ML Maths
  • Data Visualisation
  • Pandas
  • NumPy
  • NLP
  • Deep Learning
  • Web Technologies
  • HTML
  • CSS
  • JavaScript
  • TypeScript
  • ReactJS
  • NextJS
  • Bootstrap
  • Web Design
  • Python Tutorial
  • Python Programming Examples
  • Python Projects
  • Python Tkinter
  • Python Web Scraping
  • OpenCV Tutorial
  • Python Interview Question
  • Django
  • Computer Science
  • Operating Systems
  • Computer Network
  • Database Management System
  • Software Engineering
  • Digital Logic Design
  • Engineering Maths
  • Software Development
  • Software Testing
  • DevOps
  • Git
  • Linux
  • AWS
  • Docker
  • Kubernetes
  • Azure
  • GCP
  • DevOps Roadmap
  • System Design
  • High Level Design
  • Low Level Design
  • UML Diagrams
  • Interview Guide
  • Design Patterns
  • OOAD
  • System Design Bootcamp
  • Interview Questions
  • Inteview Preparation
  • Competitive Programming
  • Top DS or Algo for CP
  • Company-Wise Recruitment Process
  • Company-Wise Preparation
  • Aptitude Preparation
  • Puzzles
  • School Subjects
  • Mathematics
  • Physics
  • Chemistry
  • Biology
  • Social Science
  • English Grammar
  • Commerce
  • World GK
  • GeeksforGeeks Videos
  • DSA
  • Python
  • Java
  • C++
  • Web Development
  • Data Science
  • CS Subjects
@GeeksforGeeks, Sanchhaya Education Private Limited, All rights reserved
We use cookies to ensure you have the best browsing experience on our website. By using our site, you acknowledge that you have read and understood our Cookie Policy & Privacy Policy
Lightbox
Improvement
Suggest Changes
Help us improve. Share your suggestions to enhance the article. Contribute your expertise and make a difference in the GeeksforGeeks portal.
geeksforgeeks-suggest-icon
Create Improvement
Enhance the article with your expertise. Contribute to the GeeksforGeeks community and help create better learning resources for all.
geeksforgeeks-improvement-icon
Suggest Changes
min 4 words, max Words Limit:1000

Thank You!

Your suggestions are valuable to us.

What kind of Experience do you want to share?

Interview Experiences
Admission Experiences
Career Journeys
Work Experiences
Campus Experiences
Competitive Exam Experiences