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

📄 136% (1.36x) speedup for fibonacci in code_to_optimize_ts/fibonacci.ts

⏱️ Runtime : 3.83 microseconds 1.62 microsecondss (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 136% speedup by replacing the exponentially-complex recursive algorithm with an iterative approach that uses constant space and linear time.

Key Changes:

  • Eliminated recursive calls: The original code makes two recursive calls per invocation (fibonacci(n-1) + fibonacci(n-2)), creating an exponential call tree with O(2^n) time complexity and massive redundant calculations.
  • Iterative bottom-up computation: The optimized version uses a simple loop that computes each Fibonacci number exactly once, maintaining only two variables (prev and curr) to track the last two values needed for the next computation.

Why This is Faster:

  • No function call overhead: Recursive calls in JavaScript/TypeScript involve stack frame creation, parameter passing, and context switching. The iterative approach eliminates all of this overhead.
  • No redundant computation: In the recursive version, fibonacci(n-2) is calculated multiple times (e.g., when computing fibonacci(n-1), it recalculates fibonacci(n-2) again). The iterative approach computes each value once.
  • Better cache locality: The loop keeps two numbers in registers/L1 cache, while recursion creates scattered stack frames.

Performance Impact:
The 136% speedup at the measured input size is just the beginning—the improvement scales dramatically with larger inputs. For n=30, the speedup would be in the thousands; for n=40, it would be millions of times faster. Even for small inputs (n≤10), the elimination of function call overhead provides measurable gains.

This optimization is universally beneficial regardless of where fibonacci() is called, but especially valuable if invoked in loops or with moderate-to-large input values.

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-mklo5q5k and push.

Codeflash Static Badge

The optimized code achieves a **136% speedup** by replacing the exponentially-complex recursive algorithm with an iterative approach that uses **constant space and linear time**.

**Key Changes:**
- **Eliminated recursive calls**: The original code makes two recursive calls per invocation (`fibonacci(n-1) + fibonacci(n-2)`), creating an exponential call tree with O(2^n) time complexity and massive redundant calculations.
- **Iterative bottom-up computation**: The optimized version uses a simple loop that computes each Fibonacci number exactly once, maintaining only two variables (`prev` and `curr`) to track the last two values needed for the next computation.

**Why This is Faster:**
- **No function call overhead**: Recursive calls in JavaScript/TypeScript involve stack frame creation, parameter passing, and context switching. The iterative approach eliminates all of this overhead.
- **No redundant computation**: In the recursive version, `fibonacci(n-2)` is calculated multiple times (e.g., when computing `fibonacci(n-1)`, it recalculates `fibonacci(n-2)` again). The iterative approach computes each value once.
- **Better cache locality**: The loop keeps two numbers in registers/L1 cache, while recursion creates scattered stack frames.

**Performance Impact:**
The 136% speedup at the measured input size is just the beginning—the improvement scales dramatically with larger inputs. For n=30, the speedup would be in the thousands; for n=40, it would be millions of times faster. Even for small inputs (n≤10), the elimination of function call overhead provides measurable gains.

This optimization is universally beneficial regardless of where `fibonacci()` is called, but especially valuable if invoked in loops or with moderate-to-large input values.
@codeflash-ai codeflash-ai bot requested a review from Saga4 January 19, 2026 21:20
@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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