Minimum removals required to convert given array to a Mountain Array
Last Updated :
02 Feb, 2023
Given an array arr[] consisting of N integers???, the task is to find the minimum number of array elements required to be removed to make the given array a mountain array.
A mountain array has the following properties:
- Length of the array ? 3.
- There exists some index i (0-based indexing) with 0 < i < N – 1 such that:
- arr[0] < arr[1] < … < arr[i – 1] < arr[i]
- arr[i] > arr[i + 1] > … > arr[arr.length – 1].
Examples:
Input: arr[] = {1, 3, 1}
Output: 0
Explanation: The array itself is a mountain array. Therefore, no removal is required.
Input: arr[] = {2, 1, 1, 5, 6, 2, 3, 1}
Output: 3
Explanation: Removing arr[0], arr[1] and arr[5] modifies arr[] to {1, 5, 6, 3, 1}, which is a mountain array.
Approach 1:
The idea is to solve this problem using the Bottom-Up Dynamic Programming approach. Follow the steps below to solve the problem:
- If the length of the given array is less than 3, then the array cannot be converted to a mountain array.
- Otherwise, traverse the array and for every ith element (0 < i < N), find the length of increasing subsequence in the subarrays {arr[0], …, arr[i – 1]} and store it in an array, say leftIncreasing[].
- Similarly, find the length of the increasing subsequence in the subarray {arr[i+1], …., arr[N-1]} for every ith element (0 < i < N), and store it in an array, say rightIncreasing[].
- Find the index i (0 < i < N) which satisfies the following conditions:
- The first compulsory condition is the peak condition, which is leftIncreasing[i] > 0 and rightIncreasing[i] > 0.
- Among all indices, If leftIncreasing[i] + rightIncreasing[i] is the maximum, that index is the peak of the mountain array, say X.
- Return the result as N – (X + 1), adding one to bring the array index to length.
Illustration:
Consider the array arr[] = {4, 3, 6, 4, 5}
Therefore, leftIncreasing[] = {0, 0, 1, 1, 2} & rightIncreasing[] = {2, 1, 1, 0, 0}.
There is only one index i = 2 (0-based indexing), for which leftIncreasing[2] > 0 and rightIncreasing[2] > 0.
Therefore, X = leftIncreasing[2] + rightIncreasing[2] = 2.
Therefore, the required answer = N – (X + 1) = 5 – (2 + 3)= 2.
One of the possible solutions could be {4, 6, 5} i.e. removing 3 (arr[1]) and 4(arr[3]).
Below is the implementation of the above approach:
C++
#include <bits/stdc++.h>
using namespace std;
int minRemovalsUtil( int arr[], int n)
{
int result = 0;
if (n < 3) {
return -1;
}
int leftIncreasing[n] = {0};
int rightIncreasing[n] = {0};
for ( int i = 1; i < n; i++)
{
for ( int j = 0; j < i; j++)
{
if (arr[j] < arr[i])
{
leftIncreasing[i]
= max(leftIncreasing[i],
leftIncreasing[j] + 1);
}
}
}
for ( int i = n - 2; i >= 0; i--)
{
for ( int j = n - 1; j > i; j--)
{
if (arr[j] < arr[i])
{
rightIncreasing[i]
= max(rightIncreasing[i],
rightIncreasing[j] + 1);
}
}
}
for ( int i = 0; i < n; i++)
{
if (leftIncreasing[i] != 0
&& rightIncreasing[i] != 0)
{
result = max(result,
leftIncreasing[i]
+ rightIncreasing[i]);
}
}
return n - (result + 1);
}
void minRemovals( int arr[], int n)
{
int ans = minRemovalsUtil(arr, n);
cout << ans;
}
int main()
{
int arr[] = { 2, 1, 1, 5, 6, 2, 3, 1 };
int n = sizeof (arr) / sizeof (arr[0]);
minRemovals(arr, n);
return 0;
}
|
Java
import java.io.*;
import java.util.*;
class GFG {
public static int minRemovalsUtil(
int [] arr)
{
int result = 0 ;
if (arr.length < 3 ) {
return - 1 ;
}
int [] leftIncreasing
= new int [arr.length];
int [] rightIncreasing = new int [arr.length];
for ( int i = 1 ; i < arr.length; i++) {
for ( int j = 0 ; j < i; j++) {
if (arr[j] < arr[i]) {
leftIncreasing[i]
= Math.max(
leftIncreasing[i],
leftIncreasing[j] + 1 );
}
}
}
for ( int i = arr.length - 2 ; i >= 0 ; i--) {
for ( int j = arr.length - 1 ; j > i; j--) {
if (arr[j] < arr[i]) {
rightIncreasing[i]
= Math.max(rightIncreasing[i],
rightIncreasing[j] + 1 );
}
}
}
for ( int i = 0 ; i < arr.length; i++) {
if (leftIncreasing[i] != 0
&& rightIncreasing[i] != 0 ) {
result = Math.max(
result, leftIncreasing[i]
+ rightIncreasing[i]);
}
}
return arr.length - (result + 1 );
}
public static void minRemovals( int [] arr)
{
int ans = minRemovalsUtil(arr);
System.out.println(ans);
}
public static void main(String[] args)
{
int [] arr = { 2 , 1 , 1 , 5 , 6 , 2 , 3 , 1 };
minRemovals(arr);
}
}
|
Python3
def minRemovalsUtil(arr):
result = 0
if ( len (arr) < 3 ):
return - 1
leftIncreasing = [ 0 ] * len (arr)
rightIncreasing = [ 0 ] * len (arr)
for i in range ( 1 , len (arr)):
for j in range (i):
if (arr[j] < arr[i]):
leftIncreasing[i] = max (leftIncreasing[i],
leftIncreasing[j] + 1 );
for i in range ( len (arr) - 2 , - 1 , - 1 ):
j = len (arr) - 1
while j > i:
if (arr[j] < arr[i]) :
rightIncreasing[i] = max (rightIncreasing[i],
rightIncreasing[j] + 1 )
j - = 1
for i in range ( len (arr)):
if (leftIncreasing[i] ! = 0 and
rightIncreasing[i] ! = 0 ):
result = max (result, leftIncreasing[i] +
rightIncreasing[i]);
return len (arr) - (result + 1 )
def minRemovals(arr):
ans = minRemovalsUtil(arr)
print (ans)
if __name__ = = "__main__" :
arr = [ 2 , 1 , 1 , 5 , 6 , 2 , 3 , 1 ]
minRemovals(arr)
|
C#
using System;
class GFG
{
public static int minRemovalsUtil( int [] arr)
{
int result = 0;
if (arr.Length < 3)
{
return -1;
}
int [] leftIncreasing
= new int [arr.Length];
int [] rightIncreasing = new int [arr.Length];
for ( int i = 1; i < arr.Length; i++)
{
for ( int j = 0; j < i; j++)
{
if (arr[j] < arr[i])
{
leftIncreasing[i]
= Math.Max(
leftIncreasing[i],
leftIncreasing[j] + 1);
}
}
}
for ( int i = arr.Length - 2; i >= 0; i--)
{
for ( int j = arr.Length - 1; j > i; j--)
{
if (arr[j] < arr[i])
{
rightIncreasing[i]
= Math.Max(rightIncreasing[i],
rightIncreasing[j] + 1);
}
}
}
for ( int i = 0; i < arr.Length; i++)
{
if (leftIncreasing[i] != 0
&& rightIncreasing[i] != 0)
{
result = Math.Max(result, leftIncreasing[i]
+ rightIncreasing[i]);
}
}
return arr.Length - (result + 1);
}
public static void minRemovals( int [] arr)
{
int ans = minRemovalsUtil(arr);
Console.WriteLine(ans);
}
public static void Main(String[] args)
{
int [] arr = {2, 1, 1, 5, 6, 2, 3, 1};
minRemovals(arr);
}
}
|
Javascript
<script>
function minRemovalsUtil(arr, n)
{
var result = 0;
if (n < 3) {
return -1;
}
var leftIncreasing = Array(n).fill(0);
var rightIncreasing = Array(n).fill(0);
for ( var i = 1; i < n; i++)
{
for ( var j = 0; j < i; j++)
{
if (arr[j] < arr[i])
{
leftIncreasing[i]
= Math.max(leftIncreasing[i],
leftIncreasing[j] + 1);
}
}
}
for ( var i = n - 2; i >= 0; i--)
{
for ( var j = n - 1; j > i; j--)
{
if (arr[j] < arr[i])
{
rightIncreasing[i]
= Math.max(rightIncreasing[i],
rightIncreasing[j] + 1);
}
}
}
for ( var i = 0; i < n; i++)
{
if (leftIncreasing[i] != 0
&& rightIncreasing[i] != 0)
{
result = Math.max(result,
leftIncreasing[i]
+ rightIncreasing[i]);
}
}
return n - (result + 1);
}
function minRemovals(arr, n)
{
var ans = minRemovalsUtil(arr, n);
document.write( ans);
}
var arr = [2, 1, 1, 5, 6, 2, 3, 1];
var n = arr.length;
minRemovals(arr, n);
</script>
|
Time Complexity: O(N2), where N is the number of elements in the array
In the worst case every time we have to compare with all previous elements again.
Auxiliary Space: O(N)
For the left and right increasing array
Approach 2 : (Efficient Code)
The idea is same but by doing a slight change in the previous code we can reduce the redundant work.
The algorithm is basically works on finding the largest bitonic subsequence and after finding it subtract from the total length of the array. That will be the required answer. Below explanation is for the following.
We were making the left increasing and right increasing subsequence array and for each we are doing the same work twice. That can be done by a single function and reverse the result to our need., i.e. a slight observation upon the current scenario is, we basically need a longest increasing subsequence (LIS) and longest decreasing subsequence (LDS). And taking both of them in right direction.
For understanding it, if given array is [2 1 1 5 6 2 3 1] then the LIS array would look something like, [1 1 1 2 3 2 3 1] and if we would find the LDS array that would look like [2 1 1 3 3 2 2 1]. It can be easily achieved by the LIS function by passing the reversed array.
Below is the implementation of the algorithm :
C++
#include <bits/stdc++.h>
using namespace std;
vector< int > giveLIS(vector< int >& nums)
{
int n = nums.size();
vector< int > lis(n, 1);
for ( int i = 1; i < n; i++) {
int canAns = 1;
for ( int j = i - 1; j >= 0; j--) {
if (nums[j] < nums[i])
canAns = max(canAns, lis[j] + 1);
}
lis[i] = canAns;
}
return lis;
}
void minRemovals(vector< int >& nums, int n)
{
vector< int > lis = giveLIS(nums);
reverse(nums.begin(), nums.end());
vector< int > lds = giveLIS(nums);
reverse(lds.begin(), lds.end());
int maxi = 0;
for ( int i = 0; i < n; i++) {
if (lis[i] == 1 or lds[i] == 1)
continue ;
maxi = max(maxi, lis[i] + lds[i] - 1);
}
int ans = (n - maxi);
cout << ans;
}
int main()
{
vector< int > arr = { 2, 1, 1, 5, 6, 2, 3, 1 };
int n = arr.size();
minRemovals(arr, n);
return 0;
}
|
Java
import java.util.*;
import java.io.*;
class GFG{
static ArrayList<Integer> giveLIS(ArrayList<Integer> nums)
{
int n = nums.size();
ArrayList<Integer> lis = new ArrayList<Integer>();
for ( int i = 0 ; i < n ; i++){
lis.add( 1 );
}
for ( int i = 1 ; i < n ; i++) {
int canAns = 1 ;
for ( int j = i - 1 ; j >= 0 ; j--) {
if (nums.get(j) < nums.get(i))
canAns = Math.max(canAns, lis.get(j) + 1 );
}
lis.set(i, canAns);
}
return lis;
}
static void minRemovals(ArrayList<Integer> nums, int n)
{
ArrayList<Integer> lis = giveLIS(nums);
Collections.reverse(nums);
ArrayList<Integer> lds = giveLIS(nums);
Collections.reverse(lds);
int maxi = 0 ;
for ( int i = 0 ; i < n ; i++) {
if (lis.get(i) == 1 || lds.get(i) == 1 )
continue ;
maxi = Math.max(maxi, lis.get(i) + lds.get(i) - 1 );
}
int ans = (n - maxi);
System.out.println(ans);
}
public static void main(String args[])
{
ArrayList<Integer> arr = new ArrayList<Integer>(
List.of(
2 , 1 , 1 , 5 , 6 , 2 , 3 , 1
)
);
int n = arr.size();
minRemovals(arr, n);
}
}
|
Python3
from typing import List
def giveLIS(nums: List [ int ]) - > List [ int ]:
n = len (nums)
lis = [ 1 ] * n
for i in range ( 1 , n):
canAns = 1
for j in range (i - 1 , - 1 , - 1 ):
if nums[j] < nums[i]:
canAns = max (canAns, lis[j] + 1 )
lis[i] = canAns
return lis
def minRemovals(nums: List [ int ], n: int ):
lis = giveLIS(nums)
nums.reverse()
lds = giveLIS(nums)
nums.reverse()
lds.reverse()
maxi = 0
for i in range (n):
if lis[i] = = 1 or lds[i] = = 1 :
continue
maxi = max (maxi, lis[i] + lds[i] - 1 )
ans = (n - maxi)
print (ans)
arr = [ 2 , 1 , 1 , 5 , 6 , 2 , 3 , 1 ]
n = len (arr)
minRemovals(arr, n)
|
C#
using System;
using System.Collections;
using System.Collections.Generic;
class GFG
{
static List< int > giveLIS(List< int > nums)
{
int n = nums.Count;
List< int > lis = new List< int >();
for ( int i = 0 ; i < n ; i++){
lis.Add(1);
}
for ( int i = 1 ; i < n ; i++) {
int canAns = 1;
for ( int j = i - 1 ; j >= 0 ; j--) {
if (nums[j] < nums[i])
canAns = Math.Max(canAns, lis[j] + 1);
}
lis[i] = canAns;
}
return lis;
}
static void minRemovals(List< int > nums, int n)
{
List< int > lis = giveLIS(nums);
nums.Reverse();
List< int > lds = giveLIS(nums);
lds.Reverse();
int maxi = 0;
for ( int i = 0 ; i < n ; i++) {
if (lis[i] == 1 || lds[i] == 1)
continue ;
maxi = Math.Max(maxi, lis[i] + lds[i] - 1);
}
int ans = (n - maxi);
Console.WriteLine(ans);
}
public static void Main( string [] args){
List< int > arr = new List< int >{2, 1, 1, 5, 6, 2, 3, 1};
int n = arr.Count;
minRemovals(arr, n);
}
}
|
Javascript
function giveLIS(nums) {
let n = nums.length;
let lis = new Array(n).fill(1);
for (let i = 1; i < n; i++) {
let canAns = 1;
for (let j = i - 1; j >= 0; j--) {
if (nums[j] < nums[i])
canAns = Math.max(canAns, lis[j] + 1);
}
lis[i] = canAns;
}
return lis;
}
function minRemovals(nums) {
let lis = giveLIS(nums);
nums.reverse();
let lds = giveLIS(nums);
lds.reverse();
let maxi = 0;
for (let i = 0; i < nums.length; i++) {
if (lis[i] === 1 || lds[i] === 1)
continue ;
maxi = Math.max(maxi, lis[i] + lds[i] - 1);
}
let ans = (nums.length - maxi);
document.write(ans);
}
let arr = [ 2, 1, 1, 5, 6, 2, 3, 1 ];
minRemovals(arr);
|
Time Complexity: O(N^2), where N is the number of elements in the array
In the worst case every time we have to compare with all previous elements again.
Auxiliary Space: O(N)
For the LIS and LDS array we are making to calculate
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