Given an array arr[] where no two adjacent elements are same, find the index of a peak element. An element is considered to be a peak element if it is strictly greater than its adjacent elements. If there are multiple peak elements, return the index of any one of them.
Note: Consider the element before the first element and the element after the last element to be negative infinity.
Examples:
Input: arr[] = [1, 2, 4, 5, 7, 8, 3]
Output: 5
Explanation: arr[5] = 8 is a peak element because arr[4] < arr[5] > arr[6].
Input: arr[] = [10, 20, 15, 2, 23, 90, 80]
Output: 1
Explanation: Element 20 at index 1 is a peak since 10 < 20 > 15. Index 5 (value 90) is also a peak, but returning any one peak index is valid.
[Naive Approach] Using Linear Search - O(n) Time and O(1) Space
The simplest approach is to iterate through the array and check if an element is greater than its neighbors. If it is, then it's a peak element.
C++
#include <iostream>
#include <vector>
using namespace std;
int peakElement(vector<int> &arr) {
int n = arr.size();
for(int i = 0; i < n; i++) {
bool left = true;
bool right = true;
// Check for element to the left
if(i > 0 && arr[i] <= arr[i - 1])
left = false;
// Check for element to the right
if(i < n - 1 && arr[i] <= arr[i + 1])
right = false;
// If arr[i] is greater than its left as well as
// its right element, return its index
if(left && right) {
return i;
}
}
return 0;
}
int main() {
vector<int> arr = {1, 2, 4, 5, 7, 8, 3};
cout << peakElement(arr);
return 0;
}
C
#include <stdio.h>
int peakElement(int arr[], int n) {
for (int i = 0; i < n; i++) {
int left = 1;
int right = 1;
// Check for element to the left
if (i > 0 && arr[i] <= arr[i - 1])
left = 0;
// Check for element to the right
if (i < n - 1 && arr[i] <= arr[i + 1])
right = 0;
// If arr[i] is greater than its left as well as
// its right element, return its index
if (left && right) {
return i;
}
}
return 0;
}
int main() {
int arr[] = {1, 2, 4, 5, 7, 8, 3};
int n = sizeof(arr) / sizeof(arr[0]);
printf("%d\n", peakElement(arr, n));
return 0;
}
Java
class GfG {
static int peakElement(int[] arr) {
int n = arr.length;
for (int i = 0; i < n; i++) {
boolean left = true;
boolean right = true;
// Check for element to the left
if (i > 0 && arr[i] <= arr[i - 1])
left = false;
// Check for element to the right
if (i < n - 1 && arr[i] <= arr[i + 1])
right = false;
// If arr[i] is greater than its left as well as
// its right element, return its index
if (left && right) {
return i;
}
}
return 0;
}
public static void main(String[] args) {
int[] arr = {1, 2, 4, 5, 7, 8, 3};
System.out.println(peakElement(arr));
}
}
Python
def peakElement(arr):
n = len(arr)
for i in range(n):
left = True
right = True
# Check for element to the left
if i > 0 and arr[i] <= arr[i - 1]:
left = False
# Check for element to the right
if i < n - 1 and arr[i] <= arr[i + 1]:
right = False
# If arr[i] is greater than its left as well as
# its right element, return its index
if left and right:
return i
if __name__ == "__main__":
arr = [1, 2, 4, 5, 7, 8, 3]
print(peakElement(arr))
C#
using System;
class GfG {
static int peakElement(int[] arr) {
int n = arr.Length;
for (int i = 0; i < n; i++) {
bool left = true;
bool right = true;
// Check for element to the left
if (i > 0 && arr[i] <= arr[i - 1])
left = false;
// Check for element to the right
if (i < n - 1 && arr[i] <= arr[i + 1])
right = false;
// If arr[i] is greater than its left as well as
// its right element, return its index
if (left && right) {
return i;
}
}
return 0;
}
static void Main() {
int[] arr = { 1, 2, 4, 5, 7, 8, 3 };
Console.WriteLine(peakElement(arr));
}
}
JavaScript
function peakElement(arr) {
let n = arr.length;
for (let i = 0; i < n; i++) {
let left = true;
let right = true;
// Check for element to the left
if (i > 0 && arr[i] <= arr[i - 1])
left = false;
// Check for element to the right
if (i < n - 1 && arr[i] <= arr[i + 1])
right = false;
// If arr[i] is greater than its left as well as
// its right element, return its index
if (left && right) {
return i;
}
}
}
// Driver Code
let arr = [1, 2, 4, 5, 7, 8, 3];
console.log(peakElement(arr));
[Expected Approach] Using Binary Search - O(log n) Time and O(1) Space
If we observe carefully, we can say that:
If an element is smaller than it's next element then it is guaranteed that at least one peak element will exist on the right side of this element.
Conversely if an element is smaller than it's previous element then it is guaranteed that at least one peak element will exist on the left side of this element.
Therefore, we can use binary search to find the peak element.
Why it is guaranteed that peak element will definitely exist on the right side of an element, if its next element is greater than it?
If we keep moving in the right side of this element, as long as the elements are increasing, we will eventually reach an element that is either:
- The last element of the array, which will be a peak as it is greater than or equal to its previous element.
- An element where the sequence is no longer increasing, i.e., arr[i] > arr[i + 1], which would be a peak element.
For the same reasons, if an element is lesser than its previous element, then it is guaranteed that at least one peak element will exist on the left side of that element.
C++
#include <iostream>
#include <vector>
using namespace std;
int peakElement(vector<int> &arr) {
int n = arr.size();
// If there is only one element, then it's a peak
if (n == 1)
return 0;
// Check if the first element is a peak
if (arr[0] > arr[1])
return 0;
// Check if the last element is a peak
if (arr[n - 1] > arr[n - 2])
return n - 1;
// Search Space for binary Search
int lo = 1, hi = n - 2;
while(lo <= hi) {
int mid = lo + (hi - lo)/2;
// If the element at mid is a
// peak element return mid
if(arr[mid] > arr[mid - 1]
&& arr[mid] > arr[mid + 1])
return mid;
// If next neighbor is greater, then peak
// element will exist in the right subarray
if(arr[mid] < arr[mid + 1])
lo = mid + 1;
// Otherwise, it will exist in left subarray
else
hi = mid - 1;
}
return 0;
}
int main() {
vector<int> arr = {1, 2, 4, 5, 7, 8, 3};
cout << peakElement(arr);
return 0;
}
C
#include <stdio.h>
int peakElement(int arr[], int n) {
// If there is only one element, then it's a peak
if (n == 1)
return 0;
// Check if the first element is a peak
if (arr[0] > arr[1])
return 0;
// Check if the last element is a peak
if (arr[n - 1] > arr[n - 2])
return n - 1;
// Search Space for binary Search
int lo = 1, hi = n - 2;
while (lo <= hi) {
int mid = lo + (hi - lo) / 2;
// If the element at mid is a
// peak element return mid
if (arr[mid] > arr[mid - 1] && arr[mid] > arr[mid + 1])
return mid;
// If next neighbor is greater, then peak
// element will exist in the right subarray
if (arr[mid] < arr[mid + 1])
lo = mid + 1;
// Otherwise, it will exist in left subarray
else
hi = mid - 1;
}
return 0;
}
int main() {
int arr[] = {1, 2, 4, 5, 7, 8, 3};
int n = sizeof(arr) / sizeof(arr[0]);
printf("%d\n", peakElement(arr, n));
return 0;
}
Java
class GfG {
static int peakElement(int[] arr) {
int n = arr.length;
// If there is only one element, then it's a peak
if (n == 1)
return 0;
// Check if the first element is a peak
if (arr[0] > arr[1])
return 0;
// Check if the last element is a peak
if (arr[n - 1] > arr[n - 2])
return n - 1;
// Search Space for binary Search
int lo = 1, hi = n - 2;
while (lo <= hi) {
int mid = lo + (hi - lo) / 2;
// If the element at mid is a
// peak element return mid
if (arr[mid] > arr[mid - 1] && arr[mid] > arr[mid + 1])
return mid;
// If next neighbor is greater, then peak
// element will exist in the right subarray
if (arr[mid] < arr[mid + 1])
lo = mid + 1;
// Otherwise, it will exist in left subarray
else
hi = mid - 1;
}
return 0;
}
public static void main(String[] args) {
int[] arr = {1, 2, 4, 5, 7, 8, 3};
System.out.println(peakElement(arr));
}
}
Python
def peakElement(arr):
n = len(arr)
# If there is only one element, then it's a peak
if n == 1:
return 0
# Check if the first element is a peak
if arr[0] > arr[1]:
return 0
# Check if the last element is a peak
if arr[n - 1] > arr[n - 2]:
return n - 1
# Search Space for binary Search
lo, hi = 1, n - 2
while lo <= hi:
mid = lo + (hi - lo) // 2
# If the element at mid is a
# peak element return mid
if arr[mid] > arr[mid - 1] and arr[mid] > arr[mid + 1]:
return mid
# If next neighbor is greater, then peak
# element will exist in the right subarray
if arr[mid] < arr[mid + 1]:
lo = mid + 1
# Otherwise, it will exist in left subarray
else:
hi = mid - 1
if __name__ == "__main__":
arr = [1, 2, 4, 5, 7, 8, 3]
print(peakElement(arr))
C#
using System;
class GfG {
static int peakElement(int[] arr) {
int n = arr.Length;
// If there is only one element, then it's a peak
if (n == 1)
return 0;
// Check if the first element is a peak
if (arr[0] > arr[1])
return 0;
// Check if the last element is a peak
if (arr[n - 1] > arr[n - 2])
return n - 1;
// Search Space for binary Search
int lo = 1, hi = n - 2;
while (lo <= hi) {
int mid = lo + (hi - lo) / 2;
// If the element at mid is a
// peak element return mid
if (arr[mid] > arr[mid - 1] && arr[mid] > arr[mid + 1])
return mid;
// If next neighbor is greater, then peak
// element will exist in the right subarray
if (arr[mid] < arr[mid + 1])
lo = mid + 1;
// Otherwise, it will exist in left subarray
else
hi = mid - 1;
}
return 0;
}
static void Main() {
int[] arr = {1, 2, 4, 5, 7, 8, 3};
Console.WriteLine(peakElement(arr));
}
}
JavaScript
function peakElement(arr) {
let n = arr.length;
// If there is only one element, then it's a peak
if (n === 1)
return 0;
// Check if the first element is a peak
if (arr[0] > arr[1])
return 0;
// Check if the last element is a peak
if (arr[n - 1] > arr[n - 2])
return n - 1;
// Search Space for binary Search
let lo = 1, hi = n - 2;
while (lo <= hi) {
let mid = lo + Math.floor((hi - lo) / 2);
// If the element at mid is a
// peak element return mid
if (arr[mid] > arr[mid - 1]
&& arr[mid] > arr[mid + 1])
return mid;
// If next neighbor is greater, then peak
// element will exist in the right subarray
if (arr[mid] < arr[mid + 1])
lo = mid + 1;
// Otherwise, it will exist in left subarray
else
hi = mid - 1;
}
return 0;
}
// Driver Code
const arr = [ 1, 2, 4, 5, 7, 8, 3 ];
console.log(peakElement(arr));
Related Articles:
Similar Reads
Basics & Prerequisites
Data Structures
Array Data StructureIn this article, we introduce array, implementation in different popular languages, its basic operations and commonly seen problems / interview questions. An array stores items (in case of C/C++ and Java Primitive Arrays) or their references (in case of Python, JS, Java Non-Primitive) at contiguous
3 min read
String in Data StructureA string is a sequence of characters. The following facts make string an interesting data structure.Small set of elements. Unlike normal array, strings typically have smaller set of items. For example, lowercase English alphabet has only 26 characters. ASCII has only 256 characters.Strings are immut
2 min read
Hashing in Data StructureHashing is a technique used in data structures that efficiently stores and retrieves data in a way that allows for quick access. Hashing involves mapping data to a specific index in a hash table (an array of items) using a hash function. It enables fast retrieval of information based on its key. The
2 min read
Linked List Data StructureA linked list is a fundamental data structure in computer science. It mainly allows efficient insertion and deletion operations compared to arrays. Like arrays, it is also used to implement other data structures like stack, queue and deque. Hereâs the comparison of Linked List vs Arrays Linked List:
2 min read
Stack Data StructureA Stack is a linear data structure that follows a particular order in which the operations are performed. The order may be LIFO(Last In First Out) or FILO(First In Last Out). LIFO implies that the element that is inserted last, comes out first and FILO implies that the element that is inserted first
2 min read
Queue Data StructureA Queue Data Structure is a fundamental concept in computer science used for storing and managing data in a specific order. It follows the principle of "First in, First out" (FIFO), where the first element added to the queue is the first one to be removed. It is used as a buffer in computer systems
2 min read
Tree Data StructureTree Data Structure is a non-linear data structure in which a collection of elements known as nodes are connected to each other via edges such that there exists exactly one path between any two nodes. Types of TreeBinary Tree : Every node has at most two childrenTernary Tree : Every node has at most
4 min read
Graph Data StructureGraph Data Structure is a collection of nodes connected by edges. It's used to represent relationships between different entities. If you are looking for topic-wise list of problems on different topics like DFS, BFS, Topological Sort, Shortest Path, etc., please refer to Graph Algorithms. Basics of
3 min read
Trie Data StructureThe Trie data structure is a tree-like structure used for storing a dynamic set of strings. It allows for efficient retrieval and storage of keys, making it highly effective in handling large datasets. Trie supports operations such as insertion, search, deletion of keys, and prefix searches. In this
15+ min read
Algorithms
Searching AlgorithmsSearching algorithms are essential tools in computer science used to locate specific items within a collection of data. In this tutorial, we are mainly going to focus upon searching in an array. When we search an item in an array, there are two most common algorithms used based on the type of input
2 min read
Sorting AlgorithmsA Sorting Algorithm is used to rearrange a given array or list of elements in an order. For example, a given array [10, 20, 5, 2] becomes [2, 5, 10, 20] after sorting in increasing order and becomes [20, 10, 5, 2] after sorting in decreasing order. There exist different sorting algorithms for differ
3 min read
Introduction to RecursionThe process in which a function calls itself directly or indirectly is called recursion and the corresponding function is called a recursive function. A recursive algorithm takes one step toward solution and then recursively call itself to further move. The algorithm stops once we reach the solution
14 min read
Greedy AlgorithmsGreedy algorithms are a class of algorithms that make locally optimal choices at each step with the hope of finding a global optimum solution. At every step of the algorithm, we make a choice that looks the best at the moment. To make the choice, we sometimes sort the array so that we can always get
3 min read
Graph AlgorithmsGraph is a non-linear data structure like tree data structure. The limitation of tree is, it can only represent hierarchical data. For situations where nodes or vertices are randomly connected with each other other, we use Graph. Example situations where we use graph data structure are, a social net
3 min read
Dynamic Programming or DPDynamic 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
Bitwise AlgorithmsBitwise algorithms in Data Structures and Algorithms (DSA) involve manipulating individual bits of binary representations of numbers to perform operations efficiently. These algorithms utilize bitwise operators like AND, OR, XOR, NOT, Left Shift, and Right Shift.BasicsIntroduction to Bitwise Algorit
4 min read
Advanced
Segment TreeSegment Tree is a data structure that allows efficient querying and updating of intervals or segments of an array. It is particularly useful for problems involving range queries, such as finding the sum, minimum, maximum, or any other operation over a specific range of elements in an array. The tree
3 min read
Pattern SearchingPattern searching algorithms are essential tools in computer science and data processing. These algorithms are designed to efficiently find a particular pattern within a larger set of data. Patten SearchingImportant Pattern Searching Algorithms:Naive String Matching : A Simple Algorithm that works i
2 min read
GeometryGeometry is a branch of mathematics that studies the properties, measurements, and relationships of points, lines, angles, surfaces, and solids. From basic lines and angles to complex structures, it helps us understand the world around us.Geometry for Students and BeginnersThis section covers key br
2 min read
Interview Preparation
Practice Problem