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@codeflash-ai codeflash-ai bot commented Jan 19, 2026

📄 124% (1.24x) speedup for fibonacci in code_to_optimize_ts/fibonacci.ts

⏱️ Runtime : 3.91 microseconds 1.75 microsecondss (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 124% speedup by replacing the exponential-time recursive algorithm with a linear-time iterative approach.

Key Changes:

  • Eliminated recursive calls: The original code recursively computes fibonacci(n-1) + fibonacci(n-2), causing exponential growth in function calls—for fibonacci(10), this creates hundreds of redundant calculations.
  • Iterative loop with state tracking: The optimized version uses two variables (prev and curr) to track the last two Fibonacci numbers, computing each value exactly once in a forward loop.

Why This Is Faster:

  • Time complexity: O(2^n) → O(n). The original performs redundant recalculations (e.g., fibonacci(5) gets computed multiple times when calculating fibonacci(10)). The iterative version calculates each Fibonacci number exactly once.
  • Space complexity: O(n) call stack → O(1). The recursive version builds a call stack proportional to n, while the iterative version uses only three variables regardless of input size.
  • Reduced overhead: Eliminates function call overhead (stack frame allocation, parameter passing, return address management) which compounds with each recursive level.

Performance Impact:
Even at small values like n=10 (based on the ~4μs runtime suggesting modest input), the speedup is noticeable. For larger values (n>30), the difference becomes dramatic—exponential vs. linear growth means the original would take seconds while the optimized version remains sub-millisecond.

Best For:
This optimization excels with any n > 10, where the exponential cost of recursion becomes prohibitive. The iterative approach maintains consistent, predictable performance scaling.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 22 Passed
🌀 Generated Regression Tests 🔘 None Found
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
⚙️ Click to see Existing Unit Tests

To edit these changes git checkout codeflash/optimize-fibonacci-mklolrdo and push.

Codeflash Static Badge

The optimized code achieves a **124% speedup** by replacing the exponential-time recursive algorithm with a linear-time iterative approach.

**Key Changes:**
- **Eliminated recursive calls**: The original code recursively computes `fibonacci(n-1) + fibonacci(n-2)`, causing exponential growth in function calls—for `fibonacci(10)`, this creates hundreds of redundant calculations.
- **Iterative loop with state tracking**: The optimized version uses two variables (`prev` and `curr`) to track the last two Fibonacci numbers, computing each value exactly once in a forward loop.

**Why This Is Faster:**
- **Time complexity**: O(2^n) → O(n). The original performs redundant recalculations (e.g., `fibonacci(5)` gets computed multiple times when calculating `fibonacci(10)`). The iterative version calculates each Fibonacci number exactly once.
- **Space complexity**: O(n) call stack → O(1). The recursive version builds a call stack proportional to `n`, while the iterative version uses only three variables regardless of input size.
- **Reduced overhead**: Eliminates function call overhead (stack frame allocation, parameter passing, return address management) which compounds with each recursive level.

**Performance Impact:**
Even at small values like n=10 (based on the ~4μs runtime suggesting modest input), the speedup is noticeable. For larger values (n>30), the difference becomes dramatic—exponential vs. linear growth means the original would take seconds while the optimized version remains sub-millisecond.

**Best For:**
This optimization excels with any n > 10, where the exponential cost of recursion becomes prohibitive. The iterative approach maintains consistent, predictable performance scaling.
@codeflash-ai codeflash-ai bot requested a review from Saga4 January 19, 2026 21:33
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash labels Jan 19, 2026
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: High Optimization Quality according to Codeflash

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