⚡️ Speed up function fibonacci by 114,050%#1093
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The optimized code achieves a **1140x speedup** (from 33.8ms to 29.6μs) by replacing the exponential-time recursive algorithm with a linear-time iterative approach for the most common case: non-negative integers.
**Key Changes:**
1. **Fast Path for Integers (O(n) vs O(2^n))**: The optimization adds a check for `Number.isInteger(n) && n >= 0` and computes Fibonacci iteratively using just two variables (`a` and `b`). This eliminates the exponential explosion of recursive calls that occurs with the naive implementation—for example, `fibonacci(20)` requires ~21,891 recursive calls in the original but only 19 iterations in the optimized version.
2. **Preserves Original Semantics**: For edge cases like negative numbers, floats, or string coercions, the code falls back to the original recursive implementation (`slow()`), ensuring behavioral compatibility.
**Why This Works:**
- **Eliminates Redundant Computation**: The naive recursion recomputes the same Fibonacci values exponentially many times (e.g., `fibonacci(2)` is called thousands of times when computing `fibonacci(20)`). The iterative approach computes each value exactly once.
- **Minimal Memory Overhead**: Uses O(1) space instead of O(n) call stack depth, preventing stack overflow on larger inputs.
**Test Results Show:**
- **Large inputs benefit massively**: Performance tests computing `fibonacci(0)` through `fibonacci(30)` now complete in microseconds instead of seconds. The test expecting completion under 3000ms would pass trivially with the optimized version.
- **Small inputs remain fast**: Basic cases (0-10) already execute quickly but still benefit from eliminating function call overhead.
- **Edge cases preserved**: Tests with negative numbers, floats (e.g., `fibonacci(1.5)`), and string coercion (`fibonacci('6')`) still pass because the fallback path maintains exact original behavior.
**Impact:**
For typical workloads using integer inputs (which represent 100% of canonical Fibonacci use cases), this optimization delivers transformative performance gains while maintaining full backward compatibility for unusual inputs.
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📄 114,050% (1,140.50x) speedup for
fibonacciincode_to_optimize_js/fibonacci.js⏱️ Runtime :
33.8 milliseconds→29.6 microseconds(best of250runs)📝 Explanation and details
The optimized code achieves a 1140x speedup (from 33.8ms to 29.6μs) by replacing the exponential-time recursive algorithm with a linear-time iterative approach for the most common case: non-negative integers.
Key Changes:
Fast Path for Integers (O(n) vs O(2^n)): The optimization adds a check for
Number.isInteger(n) && n >= 0and computes Fibonacci iteratively using just two variables (aandb). This eliminates the exponential explosion of recursive calls that occurs with the naive implementation—for example,fibonacci(20)requires ~21,891 recursive calls in the original but only 19 iterations in the optimized version.Preserves Original Semantics: For edge cases like negative numbers, floats, or string coercions, the code falls back to the original recursive implementation (
slow()), ensuring behavioral compatibility.Why This Works:
Eliminates Redundant Computation: The naive recursion recomputes the same Fibonacci values exponentially many times (e.g.,
fibonacci(2)is called thousands of times when computingfibonacci(20)). The iterative approach computes each value exactly once.Minimal Memory Overhead: Uses O(1) space instead of O(n) call stack depth, preventing stack overflow on larger inputs.
Test Results Show:
Large inputs benefit massively: Performance tests computing
fibonacci(0)throughfibonacci(30)now complete in microseconds instead of seconds. The test expecting completion under 3000ms would pass trivially with the optimized version.Small inputs remain fast: Basic cases (0-10) already execute quickly but still benefit from eliminating function call overhead.
Edge cases preserved: Tests with negative numbers, floats (e.g.,
fibonacci(1.5)), and string coercion (fibonacci('6')) still pass because the fallback path maintains exact original behavior.Impact:
For typical workloads using integer inputs (which represent 100% of canonical Fibonacci use cases), this optimization delivers transformative performance gains while maintaining full backward compatibility for unusual inputs.
✅ Correctness verification report:
🌀 Click to see Generated Regression Tests
To edit these changes
git checkout codeflash/optimize-fibonacci-mkhe09u9and push.