Optimize sorter

The optimized bubble sort implements two key algorithmic improvements that significantly reduce unnecessary operations:

**1. Early Exit Optimization**: The code adds a `swapped` flag to detect when the array becomes sorted during any pass. If no swaps occur in a complete pass, the array is already sorted and the algorithm terminates early. This is especially powerful for already-sorted or nearly-sorted data, as seen in the test results where sorted lists show dramatic speedups (99924% faster for large sorted lists).

**2. Reduced Inner Loop Range**: The inner loop range changes from `range(len(arr) - 1)` to `range(len(arr) - 1 - i)`. After each outer loop iteration, the largest remaining element "bubbles up" to its correct position at the end, so there's no need to re-examine those already-sorted tail elements.

**Performance Impact**: The line profiler shows the inner loop executes ~4.98M times vs ~14M times in the original (64% reduction). While the swapping operations remain identical (same number of swaps needed), the algorithm eliminates millions of redundant comparisons. The speedup is most pronounced on:
- Already sorted data: 99924% faster (exits after first pass)
- Lists with many duplicates: 7645% faster (becomes sorted quickly)
- Mixed random data: 63-76% faster (fewer total comparisons)

These optimizations maintain bubble sort's O(n²) worst-case complexity but achieve O(n) best-case performance when data is already sorted, making it much more practical for real-world scenarios where input data may have some existing order.

Author: codeflash-ai[bot]

code_to_optimize/bubble_sort_3.py

@@ -1,8 +1,12 @@
 def sorter(arr):
     for i in range(len(arr)):
-        for j in range(len(arr) - 1):
+        swapped = False
+        for j in range(len(arr) - 1 - i):
             if arr[j] > arr[j + 1]:
                 temp = arr[j]
                 arr[j] = arr[j + 1]
                 arr[j + 1] = temp
+                swapped = True
+        if not swapped:
+            break
     return arr