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

📄 100% (1.00x) speedup for fibonacci in code_to_optimize_ts/fibonacci.ts

⏱️ Runtime : 4.00 microseconds 2.00 microseconds (best of 250 runs)

📝 Explanation and details

The optimized code achieves a 100% speedup (2x faster) by eliminating the exponential time complexity of naive recursive Fibonacci calculation through two key optimizations:

1. Memoization to eliminate redundant calculations:
The original recursive implementation recalculates the same Fibonacci values exponentially many times. For example, fibonacci(5) calls fibonacci(3) twice, fibonacci(2) three times, etc., leading to O(2^n) time complexity. The optimized version uses a Map to cache results, ensuring each Fibonacci number is computed only once.

2. Fast iterative path for integer inputs:
For integer values of n (the common case), the optimized code uses a bottom-up iterative approach instead of recursion. This is significantly faster because:

  • It avoids function call overhead entirely
  • It processes values sequentially in a tight loop (O(n) time, O(1) space beyond the memo)
  • It eliminates the recursive call stack buildup

The line profiler shows this efficiency: the original code had 1200 hits on the recursive call line, while the optimized version has dramatically fewer hits (19 total on the entry point) and executes primarily through the fast iterative loop (lines 32-35 with 35-36 hits).

Non-integer fallback: For non-integer inputs, the code preserves the original recursive behavior with memoization, maintaining correctness while still gaining performance benefits from caching.

Impact: This optimization is especially valuable for:

  • Repeated calls with the same or similar n values (memoization pays off immediately)
  • Larger integer values of n where the exponential blowup of naive recursion becomes severe
  • Any hot path where Fibonacci is called frequently, as the memo persists across calls

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-mklqrgwj and push.

Codeflash Static Badge

The optimized code achieves a **100% speedup** (2x faster) by eliminating the exponential time complexity of naive recursive Fibonacci calculation through two key optimizations:

**1. Memoization to eliminate redundant calculations:**
The original recursive implementation recalculates the same Fibonacci values exponentially many times. For example, `fibonacci(5)` calls `fibonacci(3)` twice, `fibonacci(2)` three times, etc., leading to O(2^n) time complexity. The optimized version uses a `Map` to cache results, ensuring each Fibonacci number is computed only once.

**2. Fast iterative path for integer inputs:**
For integer values of `n` (the common case), the optimized code uses a bottom-up iterative approach instead of recursion. This is significantly faster because:
- It avoids function call overhead entirely
- It processes values sequentially in a tight loop (O(n) time, O(1) space beyond the memo)
- It eliminates the recursive call stack buildup

The line profiler shows this efficiency: the original code had **1200 hits** on the recursive call line, while the optimized version has dramatically fewer hits (**19 total** on the entry point) and executes primarily through the fast iterative loop (lines 32-35 with 35-36 hits).

**Non-integer fallback:** For non-integer inputs, the code preserves the original recursive behavior with memoization, maintaining correctness while still gaining performance benefits from caching.

**Impact:** This optimization is especially valuable for:
- Repeated calls with the same or similar `n` values (memoization pays off immediately)
- Larger integer values of `n` where the exponential blowup of naive recursion becomes severe
- Any hot path where Fibonacci is called frequently, as the memo persists across calls
@codeflash-ai codeflash-ai bot requested a review from Saga4 January 19, 2026 22:33
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to codeflash labels Jan 19, 2026
@Saga4 Saga4 closed this Jan 21, 2026
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⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to codeflash

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