PreCourse-2: Exercise 1,2,3,4,5 Complete

[ankurgokhale05]

PreCourse-2: Exercise 1,2,3,4,5 Complete

[super30admin]

1. Correctness across all five exercises: The implementations of binary search, recursive quicksort, iterative quicksort, merge sort, and the slow/fast pointer middle-of-linked-list are all algorithmically correct. Each function handles the standard cases (element present, absent, first/last position, empty input) and the test suites you wrote cover these scenarios well.

2. Time complexity: All five solutions hit the expected complexities — O(log n) for binary search, O(n log n) average for both quicksort variants, O(n log n) for merge sort, and O(n) for the linked-list middle finder. The median-of-three pivot selection in both quicksort implementations is a nice touch that reduces the probability of hitting the O(n²) worst case on already-sorted or reverse-sorted input.

3. Space complexity: Binary search is O(1), the linked-list middle is O(1), and merge sort is O(n) auxiliary due to the sliced sublists. The recursive quicksort is O(log n) average / O(n) worst case for the call stack, and the iterative quicksort with the smaller-side-loop optimization correctly bounds the stack to O(log n) in the worst case — good awareness on your part.

4. Code quality and structure: The code is clean, well-commented, and each file is self-contained with its own run_tests() / run_test() harness. Docstrings clearly state the approach, complexity, and LeetCode reference. Variable names are descriptive. One minor inconsistency: Exercise_4.py uses run_test() (singular) while the others use run_tests() — not a bug, just stylistic.

5. Minor improvements / things to watch:

   • In Exercise_1.py, the function signature binarySearch(arr, l, r, x) accepts l and r but then ignores them and recomputes left = 0, right = len(arr) - 1. This works, but it's misleading — either use the passed-in bounds or drop them from the signature.
   • In Exercise_4.py, the merge sort creates new lists via slicing on every recursive call (left_half = arr[left:mid + 1]), which is O(n) extra space per level and defeats the standard in-place merge optimization. A more idiomatic version uses indices (l, r) and a single auxiliary buffer.
   • The printMiddle method in Exercise_3.py prints but doesn't return — fine for the original driver, but consider returning the value so the method is more reusable.
   • In Exercise_5.py, the iterative quicksort's smaller-side-loop optimization is correct, but the comment "O(log n) in the worst case" for space is slightly misleading — it's O(log n) worst case for the stack, which is what you meant, but worth being precise.

Overall, this is a solid, well-tested submission that demonstrates clear understanding of the algorithms and their trade-offs.