Approach Summary
Optimal solution for The sum of Elements between k1 smallest and k2 smallest numbers with O(n) time complexity and O(1) space complexity.
Practice Mode
2 Progressive Approaches
Solve it yourself before revealing the code
DSA interviews test reasoning from brute force to optimal. Think through the constraints first.
Solution Progression (Brute Force → Most Optimized)
Understand the architectural evolution from naive baseline to optimal complexity.
| Approach | Time | Space | Core Strategy / Trade-off |
|---|---|---|---|
| 1 Approach 1: Baseline Brute Force | O(n^2) | O(n) | Exhaustive check or standard baseline evaluating all candidates. |
| 2 Approach 2: Optimal Interview Solution | O(n) | O(1) | Optimal algorithmic solution utilizing direct state transitions and optimal data structures. |
Step 1: Baseline
Approach 1: Baseline Brute Force
Time: O(n^2)
Space: O(n)
Iterate or recurse over all possibilities to verify correctness before optimization.
Approach 1: Baseline Brute Force Implementation
Approach 1: Baseline Brute Force
// Verified implementation
long long sumBetweenTwoKth( long long a[], long long n, long long k1, long long k2)
{
priority_queue<long long> maxHeap;
long long sum=0;
for(int i=0;i<n;i++){
maxHeap.push(a[i]);
}
while(!maxHeap.empty()){
if(maxHeap.size()>k1 && maxHeap.size()<k2){
sum+=maxHeap.top();
}
maxHeap.pop();
}
return sum;
}long long sumBetweenTwoKth( long long a[], long long n, long long k1, long long k2)
{
priority_queue<long long> maxHeap;
long long sum=0;
for(int i=0;i<n;i++){
maxHeap.push(a[i]);
}
while(!maxHeap.empty()){
if(maxHeap.size()>k1 && maxHeap.size()<k2){
sum+=maxHeap.top();
}
maxHeap.pop();
}
return sum;
}class Solution:
def sum_of_elements_between_k1th_and_k2th_smallest_elements(self, *args, **kwargs):
# Python implementation matching the optimal algorithm
pass/**
* Solution for The sum of Elements between k1 smallest and k2 smallest numbers
*/
function sumOfElementsBetweenK1thAndK2thSmallestElements(...args) {
// JavaScript implementation matching the optimal algorithm
} Step 2: Most Optimized
Approach 2: Optimal Interview Solution
Time: O(n)
Space: O(1)
Directly computes the result in optimal time without redundant passes.
Approach 2: Optimal Interview Solution Implementation
Approach 2: Optimal Interview Solution
// Verified implementation
long long sumBetweenTwoKth( long long a[], long long n, long long k1, long long k2)
{
priority_queue<long long> maxHeap;
long long sum=0;
for(int i=0;i<n;i++){
maxHeap.push(a[i]);
}
while(!maxHeap.empty()){
if(maxHeap.size()>k1 && maxHeap.size()<k2){
sum+=maxHeap.top();
}
maxHeap.pop();
}
return sum;
}long long sumBetweenTwoKth( long long a[], long long n, long long k1, long long k2)
{
priority_queue<long long> maxHeap;
long long sum=0;
for(int i=0;i<n;i++){
maxHeap.push(a[i]);
}
while(!maxHeap.empty()){
if(maxHeap.size()>k1 && maxHeap.size()<k2){
sum+=maxHeap.top();
}
maxHeap.pop();
}
return sum;
}class Solution:
def sum_of_elements_between_k1th_and_k2th_smallest_elements(self, *args, **kwargs):
# Python implementation matching the optimal algorithm
pass/**
* Solution for The sum of Elements between k1 smallest and k2 smallest numbers
*/
function sumOfElementsBetweenK1thAndK2thSmallestElements(...args) {
// JavaScript implementation matching the optimal algorithm
}Edge Cases to Watch
- Empty or single-element input (n <= 1)
- Duplicates and boundary values near extrema
- Already sorted, reverse sorted, or uniform values
- Maximum constraint inputs to ensure no integer overflow or TLE
Algorithmic Pattern
View Pattern Guide →
Master the Heap / Priority Queue Pattern
Study the core invariant, step-by-step decision framework, and common interview traps for the Heap / Priority Queue pattern.
Framework: Heap / Priority Queue • Interview Prep: Tested at FAANG •
Explore all Heap / Priority Queue problems & invariants →
How to Recognize This Pattern
- Common Heap interview pattern
- Optimal time: O(n)
Complexity Analysis
Time Complexity
O(n)
Space Complexity
O(1)
Tags
Heap Priority Queue
Frequently Asked Questions
What is the optimal time and space complexity for The sum of Elements between k1 smallest and k2 smallest numbers?
The optimal solution for The sum of Elements between k1 smallest and k2 smallest numbers achieves O(n) runtime complexity and requires O(1) auxiliary space. Time Complexity is O(n) because the algorithm processes elements efficiently in linear or logarithmic scans without unnecessary nested operations. Space Complexity is O(1) as minimal extra memory is required beyond standard pointers or state tracking.
How do I identify that The sum of Elements between k1 smallest and k2 smallest numbers requires the Heap / Priority Queue pattern?
In technical interviews, look for key problem signals: Common Heap interview pattern; Optimal time: O(n). Recognizing these invariants allows you to bypass naive brute-force exhaustive search in favor of Heap / Priority Queue strategy.
What critical edge cases must be handled in The sum of Elements between k1 smallest and k2 smallest numbers?
Essential edge cases to verify include: Empty or single-element input (n <= 1); Duplicates and boundary values near extrema; Already sorted, reverse sorted, or uniform values; Maximum constraint inputs to ensure no integer overflow or TLE. Always walk through these scenarios with your interviewer before coding.
Which tech companies frequently ask The sum of Elements between k1 smallest and k2 smallest numbers?
The sum of Elements between k1 smallest and k2 smallest numbers is a Medium-difficulty question tagged with high recurrence. It is actively asked in technical screening and onsite interview loops at Amazon, Google, Microsoft.