#1925

Count Square Sum Triples

Easy
MathEnumerationMathematical EnumerationTwo Pointers
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Approaches

Brute ForceOptimal
Complexity Comparison
Brute ForceOptimal Solution
Time
O(n²)
O(n√n)
Space
O(1)
O(1)
💡

Intuition

Time O(n√n)Space O(1)

Instead of checking every pair (a, b), we can iterate through possible values of c and find pairs (a, b) that satisfy the equation a² + b² = c². This reduces the number of iterations significantly.

⚙️

Algorithm

6 steps
  1. 1Step 1: Initialize a counter to zero.
  2. 2Step 2: Iterate over all values of c from 1 to n.
  3. 3Step 3: For each c, iterate over possible values of a from 1 to c (since a must be less than or equal to c).
  4. 4Step 4: Calculate b as sqrt(c² - a²).
  5. 5Step 5: Check if b is an integer and if b <= n. If true, increment the counter.
  6. 6Step 6: Return the counter.
solution.py14 lines
1# Full working Python code
2import math
3
4def countSquareSumTriples(n):
5    count = 0
6    for c in range(1, n + 1):
7        for a in range(1, c + 1):
8            b = math.sqrt(c * c - a * a)
9            if b.is_integer() and b <= n:
10                count += 1
11    return count
12
13# Example usage
14print(countSquareSumTriples(5))  # Output: 2

Complexity note: The time complexity is O(n√n) because for each value of c, we iterate up to c, and calculating b involves a square root operation. The space complexity is O(1) since we are using a constant amount of space.

  • 1Square triples are symmetric; (a, b, c) and (b, a, c) count as separate valid triples.
  • 2The maximum value of c determines the range of possible a and b values.

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