Jekyll2026-05-04T01:21:35+00:00https://brun0duarte.github.io/feed.xmlblankA simple, whitespace theme for academics. Based on [*folio](https://github.com/bogoli/-folio) design. Computing Fibonacci Numbers with Dynamic Programming in C++2026-05-03T16:00:00+00:002026-05-03T16:00:00+00:00https://brun0duarte.github.io/blog/2026/fibonacci-dynamic-programmingThe Fibonacci sequence is a classic problem in computer science. The sequence is defined as follows:

\(F(0) = 0, F(1) = 1\) \(F(n) = F(n-1) + F(n-2) \text{ for } n > 1\)

A naive recursive implementation has exponential time complexity $O(2^n)$. Dynamic programming allows us to solve this in linear time $O(n)$.

Iterative Approach (Bottom-Up)

The most efficient way to compute Fibonacci numbers is using an iterative approach that saves space by only keeping track of the last two values.

#include <iostream>
#include <vector>

long long fibonacci(int n) {
    if (n <= 1) return n;

    long long prev = 0;
    long long curr = 1;

    for (int i = 2; i <= n; ++i) {
        long long next = prev + curr;
        prev = curr;
        curr = next;
    }

    return curr;
}

int main() {
    int n = 50;
    std::cout << "Fibonacci of " << n << " is " << fibonacci(n) << std::endl;
    return 0;
}

Memoization (Top-Down)

Alternatively, we can use recursion with memoization to store previously computed results.

#include <iostream>
#include <vector>

std::vector<long long> memo;

long long fib_memo(int n) {
    if (n <= 1) return n;
    if (memo[n] != -1) return memo[n];

    memo[n] = fib_memo(n - 1) + fib_memo(n - 2);
    return memo[n];
}

int main() {
    int n = 50;
    memo.assign(n + 1, -1);
    std::cout << "Fibonacci (Memoized) of " << n << " is " << fib_memo(n) << std::endl;
    return 0;
}

Dynamic programming is a powerful technique that transforms exponential problems into linear ones by avoiding redundant computations.

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