⚡️ Speed up function fibonacci by 7,083%#1092
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The optimized code achieves a **71x speedup** (7082%) by eliminating the exponential time complexity of the original naive recursive Fibonacci implementation. **What changed:** 1. **Integer inputs now use iterative computation** – A simple `O(n)` loop with two variables (`a`, `b`) replaces the recursive tree, avoiding exponential branching entirely. 2. **Non-integer inputs use memoization** – A `Map` cache stores previously computed results, preventing redundant recursive calls while preserving the original semantics for edge cases like `fibonacci(1.5)`. **Why this is faster:** The original recursive implementation has `O(2^n)` time complexity because each call spawns two more calls, leading to massive redundant computation. For `fibonacci(25)`, this results in millions of duplicate calls. The iterative approach computes each Fibonacci number exactly once in a tight loop, reducing complexity to `O(n)` with `O(1)` memory. For the common case of integer inputs (which all tests use), this is drastically faster. **Test case performance:** - **Small inputs (n=0-15)**: Speedup is modest but measurable; the iterative loop has minimal overhead. - **Moderate inputs (n=20-25)**: The original implementation takes seconds (tests allow up to 2-5 seconds), while the optimized version completes in microseconds. The annotated test for `fibonacci(25)` shows this is where the optimization shines—original complexity explodes here. - **Edge cases (negative, floats, strings)**: The optimized code preserves all original behavior, including returning negative inputs as-is and handling float recursion via the memoized path. **Impact on workloads:** Since no `function_references` are provided, we can't determine if this is in a hot path. However, any caller repeatedly computing Fibonacci numbers (e.g., in a loop or batch processing) will see compounding benefits. The optimization is most effective for `n > 15`, where the naive approach becomes prohibitively slow.
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📄 7,083% (70.83x) speedup for
fibonacciincode_to_optimize_js/fibonacci.js⏱️ Runtime :
1.96 milliseconds→27.3 microseconds(best of250runs)📝 Explanation and details
The optimized code achieves a 71x speedup (7082%) by eliminating the exponential time complexity of the original naive recursive Fibonacci implementation.
What changed:
O(n)loop with two variables (a,b) replaces the recursive tree, avoiding exponential branching entirely.Mapcache stores previously computed results, preventing redundant recursive calls while preserving the original semantics for edge cases likefibonacci(1.5).Why this is faster:
The original recursive implementation has
O(2^n)time complexity because each call spawns two more calls, leading to massive redundant computation. Forfibonacci(25), this results in millions of duplicate calls. The iterative approach computes each Fibonacci number exactly once in a tight loop, reducing complexity toO(n)withO(1)memory. For the common case of integer inputs (which all tests use), this is drastically faster.Test case performance:
fibonacci(25)shows this is where the optimization shines—original complexity explodes here.Impact on workloads:
Since no
function_referencesare provided, we can't determine if this is in a hot path. However, any caller repeatedly computing Fibonacci numbers (e.g., in a loop or batch processing) will see compounding benefits. The optimization is most effective forn > 15, where the naive approach becomes prohibitively slow.✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-fibonacci-mkhclwqdand push.