In our textbook, read 6.3 - 6.8 (the zybooks "assignment" called "Reading for HW4").
The authors do a couple things differently than we do.
First, they use while loops to traverse a tree path. It's a valuable technique to know about, but you'll notice
that we present recursion in lecture.
Why? Recursion is more general, from algorithms that only need to visit one path up to those that need to visit
the entire tree.
And recursive code is simpler, especially when you use you use the NULL dummy node and approaches that avoid
parent pointers.
So we'll require you to use recursion whenever possible, recognizing that things like iterators still require
loops.
Second, instead of static functions, we implement our algorithms in methods that have access to
this
because that more closely follows
the object-oriented paradigm used in practice.
They also present general BSTs which allow duplicates while we'll focus on using trees to implement the Set
interface (so no duplicates).
The first few are to be submitted to the drop box and the last one committed to your repository through github classroom. If either part (dropbox or repo) is late, you will be charged a late day. If both parts are early, you can gain a late day
(20 points) Calculate the exact average-case time complexity of binary search. You may use any standard implementation of binary search, e.g., textbook, wikipedia, etc. Only consider successful searches (where the item we are looking for is actually in the array) and assume all elements are equally likely to be the one we are searching for.
Hint: For each i, count how many of the array elements will be found after binary search examines exactly i array elements. (For example, the middle element is the only one to be found after examining one array element, and there are two elements that can be found after examining two array elements.) Then sum over all and divide to find the average. For the generalization, you'll use a summation. You need to convince me somehow that your summation is correct, perhaps using a table or words. Once you have done that, then you may find the following formula useful:
After you have done that, you'll get a messy expression with lots of terms in it. Take the limit as n gets large so some terms go to zero. You'll end up with a much simpler expression that should be clearly between 1 and log(n), like we said in class.
The interesting question we are trying to answer here is how much smaller is the average-case analysis compared to the worst case analysis you can find in your text- book.