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This is the lab related content for the module CS2040C (Data structures and Algorithms)

Lab 03

Here is a pdf version of the ppt I covered during the lab. And the codes that I (skimmed through) / (demonstrated live).

content.pdf (The pdf version of the ppt shown)

STLList.cpp (The STL List implementation)

STLStack.cpp (The STL Stack implementation)

STLQueue.cpp (The STL Queue implementation)

STLDeque.cpp (The STL Deque implementation) (The deque is NOT implemented using DLL (Doubly linked list) internally, it is actually implemented using the concept of dynamic arrays. This is because it supports accessing any random element in O(1), i.e DQ[x] can be accessed for any x in O(1). The broad idea of dynamic array technique is that you have a structure like vector, but each node of vector does not store an element instead it stores a fixed size array. The in-depth details are out of scope.)

Challenge Questions -

Q) Try to simulate the functions of a QUEUE, i.e push(), front(), pop() using only 2 STL stacks (NOT ALLOWED TO USE ANY ARRAYS) (You are allowed to make the operations expensive, i.e instead of O(1) they can be O(N))

Solution Stack OverFlow Link

Q) In the quicksort algorithm, let us do the following 4 variations, for each variation try to comment on the time complexity of the solution? (HINT - Making a recursion tree, helps in analysis of recursive algorithms)

  1. If we always pick the smallest element as pivot
  2. If we always pick the median as pivot
  3. If we do a coin toss, if head comes, we pick the smallest element as pivot, otherwise we pick the median.
  4. If the current subproblem is of size N, then we pick the (N/3)rd smallest element, instead of the median. (The median by convention is (N/2)th smallest element)
Solution 1. O(N^2)
2. O(NlogN)
3. O(NlogN), average case (Expected time complexity) (Proof Idea - The recursion tree depth will be expected 2logN, since on average after every 2 tries, we will get a Tails on the coin which helps us in reducing the problem by half, therefore N*2logN)
4. O(NlogN), here the base of logarithm is 3/2 instead of the classical base 2. (Proof Idea - In each recursion depth, if the current size of the problem is N, then it becomes (2/3)N or (1/3)N, in worst case it always becomes (2/3)N, but then how many times can you multiply (2/3) to N, till the time you hit 1, i.e simply logN with base of 3/2)