2091. Removing Minimum and Maximum From Array
Problem Description
In this problem, we are given an array nums
which contains distinct integers. It is 0-indexed, which means the indexing of elements in the array starts with 0. Among all the elements in the nums
array, there is one element with the lowest value, termed as the minimum, and one with the highest value, known as the maximum. Our objective is to remove these two elements from the array.
A deletion involves removing an element either from the front (beginning) of the array or from the back (end) of the array. The task is to determine the minimum number of such deletions required to remove both the minimum and maximum elements from the array.
To succeed, we need to find a strategy that minimizes the total number of deletions by intelligently deciding whether to start from the front or the back of the array, as well as considering the possibility of removing elements from both ends.
Intuition
When approaching the solution, we focus on the positions (indexes) of the minimum and maximum elements in the array. The main idea hinges upon the realization that the number of deletions needed is influenced by the relative locations of these two elements.
Firstly, we identify the indexes of both the minimum and maximum elements by scanning through the array.
Then, our strategy involves considering three scenarios:
- Remove elements from the front until both the minimum and maximum elements are deleted.
- Remove elements from the back until both elements are deleted.
- Remove some elements from the front to delete one of the elements and some from the back to delete the other one.
To achieve the minimum number of deletions, we calculate the number of deletions needed for each of the scenarios and return the smallest one.
The insight here is to use the indexes of minimum and maximum elements efficiently to minimize the number of operations, as direct removals would be inefficient for large arrays.
Learn more about Greedy patterns.
Solution Approach
The solution approach for this problem leverages a straightforward scan of the array to determine the positions (indexes) of the minimum and maximum elements.
The following steps are taken in the implementation:
-
Initialize two variables,
mi
andmx
, to store the indexes of the minimum and maximum elements, respectively. We start by assuming the first element (at index 0) is both the minimum and maximum, hencemi = mx = 0
. -
Loop through every element
num
in thenums
list, keeping track of the current indexi
. During each iteration:- If the current element is less than
nums[mi]
, updatemi
with the current indexi
because a new minimum has been found. - Similarly, if the current element is greater than
nums[mx]
, updatemx
with the current indexi
as a new maximum has been found.
- If the current element is less than
-
After identifying the indexes of the minimum and maximum elements, ensure that
mi
is less than or equal tomx
by swapping them if that is not the case. This simplifies the calculation that follows, because we will be dealing with three scenarios represented by contiguous sections of the array to be deleted. -
Calculate the total number of deletions required for each of the three above-mentioned deletion strategies:
- Strategy 1: Remove elements from the beginning (front) up to and including the maximum index (
mx
). The number of deletions would bemx + 1
, since array indexing starts at 0. - Strategy 2: Remove elements from the end (back) up to and including the minimum index (
mi
). The number of deletions here would belen(nums) - mi
. - Strategy 3: Remove elements from the front up to and including the minimum index (
mi + 1
) and from the back to the maximum index (len(nums) - mx
), combining these two givesmi + 1 + len(nums) - mx
.
- Strategy 1: Remove elements from the beginning (front) up to and including the maximum index (
-
Return the minimum value out of the three calculated strategies to achieve the minimum number of deletions needed to remove both the minimum and maximum elements from the array. This is done using the built-in
min()
function with the above mentioned three results as arguments.
The Python code is elegant and concise due to its use of list enumeration for index tracking, conditional statements for dynamic updates, and the min()
function for direct comparison.
Ready to land your dream job?
Unlock your dream job with a 2-minute evaluator for a personalized learning plan!
Start EvaluatorExample Walkthrough
Let's illustrate the solution approach with a small example:
Assume we have the array nums = [3, 2, 5, 1, 4]
.
-
We initialize
mi = mx = 0
. Currently,nums[mi] = nums[mx] = 3
. -
We start looping through the elements:
num = 2, i = 1
:2
is not less than3
, somi
remains unchanged.2
is not greater than3
, somx
also remains unchanged.num = 5, i = 2
:5
is greater than3
, so we updatemx
to2
.5
is not less than3
, somi
remains unchanged.num = 1, i = 3
:1
is less than3
, so we updatemi
to3
.1
is not greater than5
, somx
remains2
.num = 4, i = 4
:4
is not less than1
, and it's not greater than5
, so bothmi
andmx
remain unchanged.
After the loop, mi = 3
(minimum element 1
at index 3
) and mx = 2
(maximum element 5
at index 2
).
-
Since
mi
is not less thanmx
, we do not swap them. -
Calculate the number of deletions for each strategy:
- Strategy 1: Removing from the front to the maximum index
mx
ismx + 1
, which equals3
deletions. - Strategy 2: Removing from the back to the minimum index
mi
islen(nums) - mi
, which is5 - 3
, resulting in2
deletions. - Strategy 3: Removing from the front up to
mi
and from the back beyondmx
ismi + 1 + len(nums) - mx
, which is3 + 1 + 5 - 2
, totaling7
deletions.
- Strategy 1: Removing from the front to the maximum index
-
Finally, return the minimum value out of the calculated strategies:
min(3, 2, 7)
which equals2
.
Therefore, the minimum number of deletions required to remove both the minimum and maximum elements from the array [3, 2, 5, 1, 4]
is 2
. This occurs by removing the last two elements (strategy 2).
Solution Implementation
1class Solution:
2 def minimumDeletions(self, nums: List[int]) -> int:
3 # Initialize indices for the minimum and maximum value elements
4 min_index = max_index = 0
5
6 # Iterate over the list to find indices of the minimum and maximum elements
7 for i, num in enumerate(nums):
8 if num < nums[min_index]: # A new minimum element found
9 min_index = i
10 if num > nums[max_index]: # A new maximum element found
11 max_index = i
12
13 # Ensure that min_index is always less than or equal to max_index
14 if min_index > max_index:
15 min_index, max_index = max_index, min_index
16
17 # Calculate the minimum number of deletions using three strategies:
18 # 1. Delete from the front to the max element
19 # 2. Delete from the min element to the end of the list
20 # 3. Delete from both ends to the min and max elements
21 # Then return the strategy that results in the fewest deletions
22 return min(
23 max_index + 1, # Deleting from the front to the max element
24 len(nums) - min_index, # Deleting from the min element to the end
25 min_index + 1 + len(nums) - max_index # Deleting from both ends
26 )
27
1class Solution {
2 public int minimumDeletions(int[] nums) {
3 // Initialize the variables to store the index of minimum and maximum element in array
4 int minIndex = 0;
5 int maxIndex = 0;
6 // Length of the array
7 int n = nums.length;
8
9 // Iterate through the array to find the indices of the minimum and maximum elements
10 for (int i = 0; i < n; ++i) {
11 // Update the index of the minimum element if a smaller element is found
12 if (nums[i] < nums[minIndex]) {
13 minIndex = i;
14 }
15 // Update the index of the maximum element if a larger element is found
16 if (nums[i] > nums[maxIndex]) {
17 maxIndex = i;
18 }
19 }
20
21 // Make sure the minIndex is always less than maxIndex for ease of calculation
22 if (minIndex > maxIndex) {
23 // Swap minIndex with maxIndex
24 int temp = maxIndex;
25 maxIndex = minIndex;
26 minIndex = temp;
27 }
28
29 // Calculate the minimum deletions needed, considering three different scenarios:
30 // 1. Removing elements from the beginning up to the maxIndex
31 // 2. Removing elements from the minIndex to the end
32 // 3. Removing elements from both ends surrounding the min and max
33 // Return the smallest of these three options
34 return Math.min(Math.min(maxIndex + 1, n - minIndex),
35 minIndex + 1 + n - maxIndex);
36 }
37}
38
1class Solution {
2public:
3 int minimumDeletions(vector<int>& nums) {
4 int minIndex = 0; // Index of the smallest number
5 int maxIndex = 0; // Index of the largest number
6 int n = nums.size(); // The total number of elements in nums
7
8 // Loop to find indices of the smallest and largest numbers
9 for (int i = 0; i < n; ++i) {
10 if (nums[i] < nums[minIndex]) { // Check for new minimum
11 minIndex = i;
12 }
13 if (nums[i] > nums[maxIndex]) { // Check for new maximum
14 maxIndex = i;
15 }
16 }
17
18 // Ensure minIndex is smaller than maxIndex for easier calculation
19 if (minIndex > maxIndex) {
20 int temp = minIndex;
21 minIndex = maxIndex;
22 maxIndex = temp;
23 }
24
25 // Three possible ways to remove the min and max, take the minimum deletions needed
26 return min(
27 min(
28 maxIndex + 1, // Delete all from start to maxIndex, inclusive
29 n - minIndex // Delete all from minIndex to end
30 ),
31 minIndex + 1 + n - maxIndex // Delete from start to minIndex and from maxIndex to end
32 );
33 }
34};
35
1function minimumDeletions(nums: number[]): number {
2 // Find the number of elements in the array
3 const numElements = nums.length;
4
5 // If the array contains only one element, one deletion is required
6 if (numElements === 1) return 1;
7
8 // Find the indices of the minimum and maximum elements in the array
9 let minIndex = nums.indexOf(Math.min(...nums));
10 let maxIndex = nums.indexOf(Math.max(...nums));
11
12 // Calculate the leftmost and rightmost positions among the min and max indices
13 let leftIndex = Math.min(minIndex, maxIndex);
14 let rightIndex = Math.max(minIndex, maxIndex);
15
16 // Calculate the possible numbers of deletions
17 // if removing from the left towards the right
18 let deleteLeftRight = leftIndex + 1 + numElements - rightIndex;
19 // if removing from the left only
20 let deleteLeftOnly = rightIndex + 1;
21 // if removing from the right only
22 let deleteRightOnly = numElements - leftIndex;
23
24 // Return the minimum number of deletions amongst the calculated options
25 return Math.min(deleteLeftRight, deleteLeftOnly, deleteRightOnly);
26}
27
Time and Space Complexity
The time complexity of the given code is O(n)
, where n
is the length of the input list nums
. This is because the code consists of a single loop that traverses the entire list to find the minimum and maximum elements' indices, which takes linear time in the size of the input.
The space complexity of the code is O(1)
. The extra space used by the code includes only a fixed number of integer variables mi
(minimum index) and mx
(maximum index), which do not depend on the input size, therefore the space used is constant.
Learn more about how to find time and space complexity quickly using problem constraints.
Which algorithm should you use to find a node that is close to the root of the tree?
Recommended Readings
Greedy Introduction div class responsive iframe iframe src https www youtube com embed WTslqPbj7I title YouTube video player frameborder 0 allow accelerometer autoplay clipboard write encrypted media gyroscope picture in picture web share allowfullscreen iframe div When do we use greedy Greedy algorithms tend to solve optimization problems Typically they will ask you to calculate the max min of some value Commonly you may see this phrased in the problem as max min longest shortest largest smallest etc These keywords can be identified by just scanning
LeetCode Patterns Your Personal Dijkstra's Algorithm to Landing Your Dream Job The goal of AlgoMonster is to help you get a job in the shortest amount of time possible in a data driven way We compiled datasets of tech interview problems and broke them down by patterns This way we
Recursion Recursion is one of the most important concepts in computer science Simply speaking recursion is the process of a function calling itself Using a real life analogy imagine a scenario where you invite your friends to lunch https algomonster s3 us east 2 amazonaws com recursion jpg You first
Want a Structured Path to Master System Design Too? Don’t Miss This!