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

📄 122% (1.22x) speedup for fibonacci in code_to_optimize_ts/fibonacci.ts

⏱️ Runtime : 4.71 microseconds 2.12 microseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 121% speedup (2.2x faster) by replacing the exponentially complex recursive approach with an iterative solution that eliminates redundant calculations and function call overhead.

Key Optimization:
The original code uses naive recursion where fibonacci(n) recursively calls itself twice for each level, leading to O(2^n) time complexity. This creates a massive tree of duplicate calculations—for example, fibonacci(5) would calculate fibonacci(3) multiple times.

The optimized version uses iterative dynamic programming with two variables (prev and curr) to build up the Fibonacci sequence from bottom to top in a single pass. This achieves O(n) time complexity with O(1) space complexity.

Why It's Faster:

  1. Eliminates redundant computation: Each Fibonacci number is calculated exactly once instead of exponentially many times
  2. Removes function call overhead: A single loop replaces potentially thousands of recursive function calls, each with stack frame allocation costs
  3. Better CPU cache utilization: Sequential iteration with local variables is cache-friendly compared to recursive call stacks

Performance Characteristics:

  • The speedup becomes dramatically more pronounced as n increases (the 121% speedup shown is likely for a small n; for n=30+, the difference would be orders of magnitude)
  • Best for: All use cases, especially when n > 10
  • The optimization maintains identical numerical results while being significantly more efficient

This is a textbook example of converting exponential recursion to linear iteration—one of the most impactful algorithmic optimizations available.

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-mklgpc1f and push.

Codeflash Static Badge

The optimized code achieves a **121% speedup** (2.2x faster) by replacing the exponentially complex recursive approach with an iterative solution that eliminates redundant calculations and function call overhead.

**Key Optimization:**
The original code uses naive recursion where `fibonacci(n)` recursively calls itself twice for each level, leading to O(2^n) time complexity. This creates a massive tree of duplicate calculations—for example, `fibonacci(5)` would calculate `fibonacci(3)` multiple times.

The optimized version uses **iterative dynamic programming** with two variables (`prev` and `curr`) to build up the Fibonacci sequence from bottom to top in a single pass. This achieves O(n) time complexity with O(1) space complexity.

**Why It's Faster:**
1. **Eliminates redundant computation**: Each Fibonacci number is calculated exactly once instead of exponentially many times
2. **Removes function call overhead**: A single loop replaces potentially thousands of recursive function calls, each with stack frame allocation costs
3. **Better CPU cache utilization**: Sequential iteration with local variables is cache-friendly compared to recursive call stacks

**Performance Characteristics:**
- The speedup becomes dramatically more pronounced as `n` increases (the 121% speedup shown is likely for a small n; for n=30+, the difference would be orders of magnitude)
- Best for: All use cases, especially when n > 10
- The optimization maintains identical numerical results while being significantly more efficient

This is a textbook example of converting exponential recursion to linear iteration—one of the most impactful algorithmic optimizations available.
@codeflash-ai codeflash-ai bot requested a review from Saga4 January 19, 2026 17:51
@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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