509. Fibonacci Number
Problem Description
The Fibonacci numbers, commonly denoted F(n) form a sequence, called the Fibonacci sequence, such that each number is the sum of the two preceding ones, starting from 0 and 1. That is,
F(0) = 0, F(1) = 1F(n) = F(n - 1) + F(n - 2), forn > 1.
Given n, calculate F(n).
Example 1:
Input: n = 2
Output: 1
Explanation: F(2) = F(1) + F(0) = 1 + 0 = 1.
Example 2:
Input: n = 3
Output: 2
Explanation: F(3) = F(2) + F(1) = 1 + 1 = 2.
Example 3:
Input: n = 4
Output: 3
Explanation: F(4) = F(3) + F(2) = 2 + 1 = 3.
Constraints:
0 <= n <= 30
Solution
Python Solution
class Solution:
def fib(self, n: int) -> int:
if n <= 1:
return n
a, b = 0, 1
for _ in range(2, n + 1):
a, b = b, a + b
return b
Time Complexity: O(n)
Where n is the input number. We need to iterate n times.
Space Complexity: O(1)
We only use two variables regardless of the input size.
Java Solution
class Solution {
public int fib(int n) {
if (n <= 1) {
return n;
}
int a = 0, b = 1;
for (int i = 2; i <= n; i++) {
int temp = b;
b = a + b;
a = temp;
}
return b;
}
}
Time Complexity: O(n)
Where n is the input number. We need to iterate n times.
Space Complexity: O(1)
We only use two variables regardless of the input size.
C++ Solution
class Solution {
public:
int fib(int n) {
if (n <= 1) {
return n;
}
int a = 0, b = 1;
for (int i = 2; i <= n; i++) {
int temp = b;
b = a + b;
a = temp;
}
return b;
}
};
Time Complexity: O(n)
Where n is the input number. We need to iterate n times.
Space Complexity: O(1)
We only use two variables regardless of the input size.
JavaScript Solution
/**
* @param {number} n
* @return {number}
*/
var fib = function(n) {
if (n <= 1) {
return n;
}
let a = 0, b = 1;
for (let i = 2; i <= n; i++) {
[a, b] = [b, a + b];
}
return b;
};
Time Complexity: O(n)
Where n is the input number. We need to iterate n times.
Space Complexity: O(1)
We only use two variables regardless of the input size.
C# Solution
public class Solution {
public int Fib(int n) {
if (n <= 1) {
return n;
}
int a = 0, b = 1;
for (int i = 2; i <= n; i++) {
int temp = b;
b = a + b;
a = temp;
}
return b;
}
}
Time Complexity: O(n)
Where n is the input number. We need to iterate n times.
Space Complexity: O(1)
We only use two variables regardless of the input size.
Approach Explanation
The solution uses an iterative approach with constant space:
- Key Insights:
- Base cases
- Variable swapping
- Iterative approach
- Space optimization
- Algorithm Steps:
- Handle base cases
- Initialize variables
- Iterate and update
- Return result
Implementation Details:
- Variable tracking
- Efficient swapping
- Loop control
- Memory usage
Optimization Insights:
- Constant space
- No recursion
- Early returns
- Minimal variables
Edge Cases:
- n = 0
- n = 1
- n = 2
- Maximum n