Data Structures Study Group

You can contact me any time via email at zac (dot) blanco (at) I will do my best to respond in a timely manner.

These notes were written during the Spring of 2017. I do my best to update/improve them when I can. Want to add or contribute? Send me an email or create an issue on GitHub.


Session 1 - Big O and Linked Lists

Problem Set 1

In this session we went over

Data structures Topics

Linked Lists

A computer requires memory every time you create a variable. Simple variables (primitives) are special built-in types that require only small amounts of space (usually architecture specific) when the computer creates them.

However when we create objects like linked list nodes the way the information is stored is not the same.

If you create a class LLNode<T> and try to instantiate and then print the class to the console with System.out.println you’ll find that the computer will print a bunch of gibberish. That’s due to the fact that when you instantiate an object, the object in code is actually referenced by a memory location. These references to memory locations are called pointers. The numbers and letters that look like gibberish are actually the ASCII values which represent the location in memory that the object resided.

The neat thing is that we can create pointers which reference other pointers within objects that are referenced by pointers themselves!

The most basic linked list node is comprised of 2 basic pieces.

In code, this might look like

public class LLNode {
  int data;
  LLNode next;
  public LLNode(int data, LLNode next){ = data; = next;

Then you could create a pointer to a node by the following:

public static void Main(String[] args) {
  LLNode ptr1 = LLNode(0, null); // This is a variable which *points* to an LLNode
  LLNode ptr2 = LLNode(10, ptr1); // This LLNode set its "next" variable to be ptr1.

  // == ptr1 ==> True
  // == ==> True

A linked list is then essentially just a number of these nodes linked together. Hence the name Linked List

A linked list object typically needs to somehow point to the beginning or head of the list. This is done by having a variable within the list list object called head.

Adding to a Linked List

The simplest way to add to a linked list is to change the reference of the head node to the new node you are trying to add. Then change the reference of the head pointer to the new node. This ensures no data is lost

To add a node pseudo-code:

The runtime for this operation is \(O(1)\) because the the size of inputs (number of items in the list) does not play a factor in the algorithm to add a node.

public static void Main(String[] args) {
  LLNode head = LLNode(0, null); // This is a variable which *points* to an LLNode
  head = addNode(12, head);
  head = addNode(15, head);

  //The list now contains three items
  //15 --> 12 --> 0 

public static LLNode addNode(int data, LLNode head) {
  LLNode new = new LLNode(12, head);
  return new;

Traversing a Linked List

So now we know how to add a bunch of nodes to our linked list. How do we traverse over a linked list?

It’s actually quite simple. In order to traverse a linked list, all we need is a basic for/while/do while loop and an understanding of how the list is structured.

First we need to create a temporary node as being equal to the head of the list. Then we set the temporary node equal to its “next” value, so long as the value of “next” is NOT null.


    public static void Main(String[] args) {
      LLNode head = LLNode(0, null); // This is a variable which *points* to an LLNode
      head = addNode(12, head);
      head = addNode(15, head);

      //The list now contains three items
      //Traversal result: 15 --> 12 --> 0 

    public static LLNode addNode(int data, LLNode head) {
      LLNode new = new LLNode(12, head);
      return new;

    public static void traverse(LLNode head) {
      LLnode tmp = head;
      while (tmp != null) { 
        System.out.print( + " --> ");
        tmp =;
      System.out.println( "\\" ); //Adds newline and prints '\'

Because we have a conditional check inside of a while loop, the runtime for a traversal of a linked list is going to be proportial to the number of items in the list. If we assume the list to be of size \(n\), then the Big-O runtime of a traversal will be \(O(n)\).

Searching in a Linked List

Fortunately, searching in a linked list is very similar to traversal. Instead of printing out items in the list on each iteration we must check whether or not the item we are currently pointing to with tmp is holds the data we’re searching for.

A simple search function might look something like

//Returns true/false if an item existed in the linked list.
public static Boolean search(int searchData, LLNode head) {
  LLNode tmp = head;
  while (tmp != null) {
    if ( == searchData) {
      return true;
    tmp =;
  return false; //False if we complete the loop

This is where big-O analysis can become trickier. If we look at the best case scenario, where the item we search for appears at the very beginning of the list then the total runtime we can estimate to be \(1\).

If the item appears in the, say, 14th place in the list, then the total runtime is \(14\).

In big-O we must always consider the worst possible scenario in terms of runtime. For searching a list this means we assume the wrost case to be when we have to compare every item in the list to our search parameter. In the case of an arbitrarily sized list of \(n\) items, the search becomes \(O(n)\).

Deleting in a Linked List

Deleting an item in a linked list is probably going to be the most complex operation that we will cover so far. This is due to the fact that not only does it incorporate the traversal/search from before, but also ends up requiring us to move and change pointers in order to remove an item from the list.

The basic steps to deleting a node in a linked list are:

However, now that we are modying our data structure (the linked list), we need to account for special cases. These are cases which we need to think about in order to write good code. This ensures that your methods will not result in NullPointerExceptions or fail to perform the specified task correctly. Identifying these cases is the first thing that you should think about before writing code.

In the case of deleting a node we have to consider:

The code for this isn’t terribly complicated but it builds on the topics covered earlier. Also note that the algorithm that I am describing only deletes the first occurrence of the item. It does not account for duplicates.

//Returns the new head of the list
public static LLNode delete(int data, LLNode head) {
  if (head == null) {
    return head;
  } else if ( == data) {
    return; //This removes the pointer to head and will end up being cleaned up by the garbage collector.

  //General case
  LLNode curr =; //head is definitely not null.
  LLNode prev = head;
  while( curr != null ) {
    if ( == data) { =; //Forces prev pointer to (removes reference to curr)
    prev = curr; //The order of these instructions is important.
    curr =;
  return head;


To analyze the runtime we see that we still have to perform a search over the list. In the worst case this results in us having to traverse over the entire list before deleting an item which gives us \(O(n)\).

Singular Linked Lists v.s. Circular Linked List

So far I have only explored singular linked lists in which we mark the end of the list by having the last node in the list have a next pointer of null.

However this leaves a pointer’s worth of information empty that could be utilized by the system to possibly make operations more efficient. A circularly linked list allows us to utilize that pointer memory by pointing it back to the end (or beginning) of the list.

In one implementation of a circularly linked list instead of having a head pointer, might replace that with a pointer named rear which will always point to the last item in the list. The field of the rear pointer would then pointer to the beginning of the list, or the head (but without using an explicit head pointe).

In this way, with a single pointer we are able to find the head and tail of a list.

This is quite a simple modification, but it does modify the code that we would use for a traversal. One option to do a traversal of the list is shown below:

public static void traverse(LLNode rear) {
  LLNode tmp =; //Actual head

  do {
    System.out.println( + " --> ");
    tmp =;
  } while (tmp !=; //Stop when we get to the front again.
  // Remember there's no null pointers so NPE isn't an issue

This is just one way of traversing over the list. The runtime of this is no different than that of the normal linked list. However by using a circular linked list this allows us to have \(O(1)\) when adding to the list at the front and the back of the list.

With a non-circular linked list the time to append to the rear of the list is \(O(n)\) and \(O(1)\) for the front. In a circular linked list the time to append to the front and rear is simply \(O(1)\). This is useful for stacks and queue’s which we’ll explore soon.

Session 2 - Common Elements, Linked Lists, Stacks, Queues, Generics and Exceptions

Merging Common Elements of Two Sorted Linked Lists

This comes from problem number 6 of problem set 2.

Imagine 2 sorted linked lists

1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]

Imagine we want to create a list of all the common elements.

The better approach: Let’s use the fact that the lists are pre-sorted to our advantage.

We know that if we’re looking for an item from the first list and within the 2nd list that if we find an item in the 2nd list which is greater than the item we’re looking for, then we can immediately stop our search for that item because if we didn’t find it already we know that it won’t exist later on in the list due to the fact that the lists are sorted.

So let’s take our lists and look for the value 2. We use a single pointer on each list.

1st LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
2 is not equal to 1. Increment the 2nd list pointer.

2nd LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
3 is NOT equal to 2, BUT 3 is greater than 2, so we increment the list 1 pointer because we now know that 2 cannot exist in list 2 due to the sorted order property.

Okay so now we get the idea so let’s try to look for 4. We increment the list 1 pointer from the last iteration.

3rd LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
We see 3 is not equal to 4. 3 is also less than 4. Increment list 2 pointer.

4th LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
We see that 4 and 4 ARE EQUAL. This means we add 4 to our new linked list. We also increment both pointers since we found a match.

5th LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
6 and 8 are not equal. 6 is still less than 8. Increment list 2 pointer.

6th LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
We see that 8 and 8 ARE EQUAL. Add 8 to the new common elements list. Increment both pointers.

7th LOOP
1st List: [2] -> [4] -> [8]
2nd List: [1] -> [3] -> [4] -> [6] -> [8] -> [16]
We find that list 1 pointer is set to null, so we can't possibly have any more matches. break out of the loop and return the list of common elements.

So that’s a basic example of running through the algorithm. The runtime is \(O(m + n)\) which is far, far better than \(O(mn)\). I encourage you to try to write to code to solve this problem. It is very similar to a merge on two sorted arrays that is done in mergesort.

Linked List Review

We’re going to now assume you have a basic understanding of pointers and the operations on linked lists.

As a quick review

There are more but those are probably the most basic.


Stacks are probably the first real data structure that you encounter in. Linked lists aren’t inherently that useful unless you utilize the properties of a stack (or queue).

So to understand the properties of a stack, first imagine a stack of cafeteria trays. A cafeteria will constantly be cleaning these trays and placing them on top of one another. The customers (or students) who use the cafeteria will need trays. When they go to the stack of clean trays to grab one for their food, typically the first tray to be taken will be the one on the top of all of the other trays. The one below that will then be the top tray. It will be taken, then the next one and so on.

So as you can imagine as trays get cleaned and put on top the first ones to be taken will be the last trays to be put on the stack. This is exactly how the stack operates. It has the property of LIFO (Last-In First-Out). This means that the most recent item to be put on a stack is the first one to be put on.

Stacks can be implemented as linked lists. They really only contain two or three new operations which aren’t that hard to implement. They are:


The push operations adds a new item to the stack. This not only adds the new item but changes the head as well. It’s pretty much exactly the same as the \(O(1)\) add that we did before for normal linked lists.


Slightly more advanced but still incredibly simple, the pop() method will remove an item from the stack. However it doesn’t just remove any item. It will remove whatever item is currently at the head of the linked list. So imagine the following stack:

5   <-- HEAD

pop() ---> returns 5
// The stack now becomes:
4   <-- HEAD

pop() --> returns 4
77   <-- HEAD


peek() will return the same element as pop(), however instead of removing it from the stack, all of the items remain in tact. The stack is not modified but the node value at the top of the stack will be returned. Example:

1   <-- HEAD

peek() --> returns 1
// Stack is

So essentially a stack is a data structure which can be implemented as a linked list that has the LIFO property described above. The methods push, pop, and peek are all available and satisfy the LIFO behavior.


Fun Fact: Queue is one of the few words in the English language that has 4 vowels in a row.

A queue is another type of data structure which is very similar to the stack in the regard that it can be implemented as a linked list. However instead of having push() and pop() we have enqueue() and dequeue()

The difference between a stack and a queue is that a stack has the LIFO property and a queue has the FIFO property. FIFO stands for First-In First-Out. You can find an example of queues every single day whenever you visit a store or stand in a line. Every time you get in line at the grocery store you are part of a queue! Think about it. The first person to get in line to check out at a grocery store is always going to be the first one to get out of that line. As more people get in they have to wait while the people in front of them check out. This satisfies the property of FIFO which is why it is an example of a queue.


The enqueue() operation of a queue is similar to a stack’s push. You can enqueue the new items on the the front of list and simply just change the lists front pointer. It is is also possible to implement the queue operations (as well as stacks) with circular or doubly linked lists, however the actual implementation will vary depending on the type of underlying list structure.


The dequeue() operation is analogous to the stack’s pop(). It will return the next item to be deleted from the queue. In a null terminated queue where we always add to the front, this operation takes \(O(n)\) time because we have to traverse the list all the way to the end to delete and return the last item.

You might think that it might be able to be fast if you use a circular linked list but the problem of reassigning the rear pointer still requires us to traverse the entire list again to find what the 2nd to last item is so we still have \(O(n)\) run time to dequeue.


The peek() operation in the queue performs the same type of action as in the stack, except that instead of simply returning the first item we return the last, or the item that would have been dequeued had dequeue() been called. This operation takes \(O(n)\) on a null-terminated linked list but actually only requires \(O(1)\) on circular linked list.


Generics are a special construct of the Java programming language that allows you to make object-agnostic classes that you can pass parameters to when instantiating the object. If that just sounded like a bunch of jargon don’t worry I’ll explain in more lay terms.

Basically when you have a linked list class, you might want to store different types of data in each node. Maybe in one list you want int while in the other one you want a float, a double, or maybe even a boolean. But if we didn’t have generics you would need to create a new class with the same method implementations for every type! Now obviously that’s just silly, take lots of time and effort, and is simply not good programming practice. Copy and pasting code is never a good idea.

So the developers of Java language came up with the idea of generics these allow you to define classes where you are allowed to pass different objects as parameters and use them as data.

Take the following linked list node class as an example:

public class Node<T extends Comparable<T>>  {
  public static void main(String[] args) {
    System.out.println("hello world");
    Node<Integer> a = new Node<Integer>(12, null);
    Node<Integer> b = new Node<Integer>(15, null);
    Node<Integer> c = new Node<Integer>(12, null);
    Node<Double> d = new Node<Double>(12.0, null);
    Node<Integer> e = new Node<Integer>(15, null);
    Node<Integer> f = new Node<Integer>(12, null);

  // By having 'extends Comparable<T>` means we can use the compareTo method
  public T data;
  public Node<T> next;
  public Node(T data, Node<T> next) { = data; = next;

  public boolean equals(Node<T> other) {


So now when you define a new Node object you can specify the type and utilize it within other types of objects without having to rewrite the methods.


Exceptions are special types of errors. Exceptions are used in order to notify a user about the reason why a program may have crashed or when an invalid instruction was being attempted to be executed. Two examples of common exceptions which might occur are NullPointerException this arises from trying to perform operations on a pointer which has a null value. Another example of an exception that you might see is ArithmeticException which is thrown when something like dividing by zero occurs. It is an operation that would normally just shut down the program and cause it to end. Another one might be IndexOutOfBoundsException when accessing array indices

However language developers are smart and we know that shutting down the program immediately upon an error is not really the best thing to do so they invented this try-catch statement in order to catch exceptions and attempt to handle them.

See below for an example

public static void main(String[] args) {

//Linear search with try/catch

int[] nums = {1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144, 169, 196, 225}

int ind = 0
//search for 255
try {
  while (nums[ind] != 255) { //eventually causes index out of bounds

} catch (IndexOutOfBoundsException e) {
  ind = -1; // Handle by doing some specified behavior
//Continue program.

Session 3 - ArrayLists, Modulo Operator, Bounded Queue, Recursion, Writing Code

Stacks and Queues - The methods for stacks and queues were covered above. Please see the notes there


ArrayLists in Java act like a linked list object. They have the same methods that you might find in a linked list, but the underlying structure which all the methods are performed on is an array instead of linked list.

The list then might be represented in the following way:

| 12 | 10 | 14 |    |    |    |

So instead of having nodes with pointers the rear of the list simply points to the last index where an item was added.

When you add a new item you simply just put it into the index of the array. There’s no need to deal with moving pointers around.

What happens when the array is full?

So when the array begins to fill up with items we get to a point where all of indices are filled with new items. It is impossible to add a new item to an array when all indices are filled. In order to fix this we must change the size of the array. But we know that it’s actually impossible to change the size of an array so we must do the following to add a new item to full ArrayList

This process allows us to enlarge the size of the list but takes much more time than a normal add. It takes \(O(n)\) every \(2^n\) additiongs to the list.

The benefits of using an array list mainly have to do with the \(O(1)\) access time that allows for access of a specific index. However for other operations such as insert or delete we don’t gain any performance benefits because we have to shift all of the elements over. Take for example the following

| 12 | 10 | 14 | 11 | 12 | 8  |
Empty Space
| 12 | 10 | 14 |    | 12 | 8  |
Left Shift
| 12 | 10 | 14 | 12 | 8  |    |
                  SHIFT LEFT

The same type of procedure would need to occur for an insertion as well. So even though to do get the performance benefit on accesses, other operations still take on order of \(O(n)\) time.

The Modulo Operator

The modulus operator is an interesting one that is not often encountered in typical mathematical courses. In programming it is often represented as %.

The function of the modulus operator is to return the remainder of the first operator divided by the second operator.


5 % 2 ==> 1
4 % 3 ==> 1
9 % 6 ==> 3

However while returning the remainder could be useful in some cases it might be important to note the properties of the modulus operator that make it like a cyclic counter. For example take the following series

0 % 5 ==> 0
1 % 5 ==> 1
2 % 5 ==> 2
3 % 5 ==> 3
4 % 5 ==> 4
5 % 5 ==> 0
6 % 5 ==> 1
7 % 5 ==> 2
8 % 5 ==> 3

Notice how after reaching 5 that the values wrap around back to 0. This makes it useful for ensuring values to be placed within a certain range. It is also possible to use in the bounded queue which we will talk about in the next section.

Bounded Queues

A bounded queue is simply a queue which has a limited amount of items which can be stored in a single time. This can be useful if the underlying list implementation is an array because then we limit the amount of memory that is taken up. We also don’t have to perform array reallocations and copies when adding new items because we will know the size of the queue beforehand.

In terms of implementation the bounded queue can be implemented by using an ArrayList of by list Nodes with next pointers.


Recursion will be a very important concept when it comes to implementing certain structures that we will encounter later on in the course.

The basic idea behind recursion is that we break a problem into smaller sub problems and find a way to solve the smaller sub problems and use those solutions in order to come to the solution of the main problem.

When attempting to solve a problem recursively we always want to first try to identify the base cases for the problem, and then use the base cases to then find the general solution.

One of the most basic examples of recursion is the Fibonacci series. The series is given by the formula:

\[F_n = \begin{cases} 0, & n = 0 \\ 1, & n = 1 \\ F_{n-1} + F_{n-2}, & n > 1 \end{cases}\]

We can see from the formula the base cases are when \(n = 0, 1\) because they are defined as simply being equal to a value. Anything greater than that we need the previous two fibonacci numbers. We can treat the solution to finding these numbers as their own fibonacci number problem.

This allows us to break down the problem and solve it with a relatively simple set of code:

public int fib(int n) {
  switch(n) {
    case 0:
      return 0;
    case 1:
      return 1;
      return fib(n-1) + fib(n-2);

The most important part of a recursive algorithm is the use of the algorithm within itself to solve the solution. You should find that many recursive solutions will have small amount of code, but the underlying logic may be confusing or hard to understand (see the answer to the problem on recursively reversing the items in a linked list from problem set 3).

General Coding Tips for Assignments

Lastly, I just wanted to go over some guidelines and tips which might be helpful for completing the programming assignments.

Tip 1: When writing code you should always be mindful of the structures that you are using and why you are using them. Which is more efficient? Is one easier to implement than another? What is the time tradeoff to implementing one over another? Are there caveats to using one algorithm/structure over another.

These are just some basic questions you should be thinking about when you approach the projects. They can help guide your thinking and problem solving.

Tip 2: The general case is important, but the edge cases should never be forgotten.

Understanding the general algorithm for a certain data structure is good. You should be able to implement it relatively easily. However when it comes to writing the code and the methods within your assignments you should always be sure that they cover not just the general cases but all cases. For linked lists this might mean empty lists, one item lists, etc. Make sure that all of the edge cases are covered. You’ll lose a lot of points if they are not.

Tip 3: Start EARLY.

This one might be common sense but some people just like to torture themselves by submitting assignments just minuted before the deadline. Remember if you submit early you can always go back and correct any mistakes that you might come across in the time between your submission and deadline.

I highly recommend at least starting the assignments as soon as they are released. Take about an hour a day per week and you should be able to finish them with plenty of time to spare with no stress. It also gives you lots of time to check for errors and test your code.

This week we discussed how to derive the average case runtime of a sequential and binary search algorithms.

First off, we defined the average runtime of an algorithm to be defined by the following equation:

\[\sum\limits_{\text{case }\in\text{cases}} t(\text{case})\times P(\text{case})\]

Basically what this tells us is the average runtime is affected by the number of different outcomes (or cases) of the algorithm and the probability of each one of those cases.

If otherwise stated, it can be assumed that every algorithm outcome has equal probability. That is if there are \(n\) possible outcomes, the probability of each outcome is simply \(\frac{1}{n}\).

After that it is simply a matter of determining the runtime for each case.

If you sum up the products of those two over every possible case you can get the average runtime. For searching algorithms like sequential and binary search we must consider the time when the search results in a successful match, but also when the search fails.

So let’s first explore sequential search

Sequential Search Analysis

Imagine given an array

   0    1    2    3
|    |    |    |    |

First we define \(n\) to be the number of elements in the array.

If we assume an unordered array then sequential search has \(n + 1\) possible ending locations. There are \(n\) places to end the search successfully and \(1\) to end the search on a failure.

Let’s first determine how many comparisons are required at any given array index location. If we assume the following method

public boolean search(int[] A, int target) {
  for(int i = 0; i < A.length; i++) {
    if(A[i] = target) { return true; }
  return false;

So if we don’t count the for-loop comparisons

etc.. If you continue the pattern given any index \(i\) of the array, the number of comparisons to end a successful search on the index is \(i+1\).

So if we are to generalize that we can say \(t(i) = i+1\)

Then if we account for the probability of each case, we get that they are all of equal probability. So for \(n\) different items, each possible ending location for the search has \(1/n\) probability.

If we use the formula from earlier

\[\sum\limits_{\text{case }\in\text{cases}} t(\text{case})\times P(\text{case})\]

Then we can apply the formulas we found for the probability and time

\[\sum\limits_{i = 0}^{n-1} t(i)\frac{1}{n} = \sum\limits_{i = 0}^{n-1} \frac{i+1}{n} = \frac{1}{n}\sum\limits_{i = 0}^{n-1} i+1\]

If we evaluate the summation we get that

\[\frac{1}{n}\sum\limits_{i = 0}^{n-1} i+1 = \frac{1}{n} (\cdot \frac{n}{2} \cdot (n + 1))\] \[\frac{1}{n} (\cdot \frac{n}{2} \cdot (n + 1)) = \frac{n}{2} + \frac{1}{2}\]

Now if we look at the possible failure cases we get that there is really only one failure case which occurs regardless of the size of the array which occurs at the very end of the array. However the number of comparisons does not change from the number of comparisons required for a successful search at \(i = n-1\) (the last index) because we are not counting the for loop comparison. The number of comparisons for a failed search is \(n\)

So the number of failed cases is simply 1. The probability of that case occurring of all failures is also 1 (because it’s the only possible failure):

\[\sum\limits_{i = 1}^{1} 1 \cdot (n) = n\]

So then we get

Average runtime for success: \(\frac{n+1}{2}\), n possible cases

Average for failure: \(n\), 1 possible case

If we are to average these two numbers together we must do so with a weighted average because the actual number of cases where each occur is different. We should note that this combination assumes the probability of failing a search is the exact same probability as a successful search at any single location. We’re just making that assumption for this particular analysis of sequential search. Other analyses may differ in their assumptions.

So then the total average runtime would be

\[\text{Avg. Runtime} = \frac{\text{success runtime}\times\text{number of success cases} + \text{failure runtime}\times\text{fail cases}}{\text{total number of cases}}\] \[\text{Avg. Runtime} = \frac{\frac{n+1}{2}n + n\cdot 1}{n + 1} \\ = \frac{n^2 + 3n}{2n + 2} = \frac{n}{2} + 1 - \frac{2}{n+1}\]

Binary Search Analysis

Now we’re going to apply the same concept to binary search.

Given an array with binary search we know the number of comparisons for a successful search is going to be based on the midpoint of the current subsection of the array and the number of previous comparisons. For each iteration of binary search we first check if the midpoint is our target, then, if not we check if the target is greater/less than.

See the following for an example of the number of comparisons.

Index      0    1    2    3    4    5    6
         |    |    |    |    |    |    |    |
# Comp.    5    3    5    1    5    3    5
Fail Pts ^    ^    ^    ^    ^    ^    ^    ^

The way that binary search differs from sequential search is that there are many more failure points. Note that instead of simply failing the search at the end of the array, the search can fail at any point in the array in between two numbers or at the ends.

We see from the diagram above that there are 8 failure points for 7 items. This means we have \(n\) possible places to successfully end the search. Then there are \(n+1\) places where the search could fail.

So we can perform the analysis by first looking at each case (success and failure) separately.


We note that there are \(n\) possible ways to finish a search where we find an item. To find the maximum number of comparisons that would be required for any search it is simply \(2log_2(n + 1) - 1\)

So if we take \(k\) to be the number of levels of the search comparison tree that would result from this array we get something like \(n = 2^k - 1\), giving \(k = log_2(n+1)\)

However we want \(k\) to be an integer which represents the number of levels in the tree so we will set \(k = \lfloor log_2(n+1) \rfloor\) which means we always round down to the lowest integer (Sometimes known as truncating the decimal)

But we know that at the last level, the tree may not have a full set of leaves. So when performing the summation we must split up the last level from the rest of the summation. And note that for any given level of the tree (start from \(i = 0\)) and treating the root as height = 0, then the equation to find the number of comparisons at any given height, \(i\), is equal to \(2i + 1\), and the number of nodes for a given height in the tree at any level (up to and including the \(k-1\)-st level) is equal to \(2^i\) so then our \(\text{Time}\times\text{Probability}\) at any level is \((2i + 1)2^i\)

\[\text{Avg. Success} = \frac{1}{n}\left(\sum\limits_{i=0}^{k-2} ((2i + 1)2^i) + ( n - (2^{k-1} - 1))(2(k)+1)\right)\]

The term \((n - (2^{k-1} - 1))(2(k)+1)\) is for the number of leaves that are present in a given list that do not complete a full binary comparison tree level. This is found by taking \(n\) (the number of total items) and subtracting the total number of other nodes.

We then multiply by the number of comparisons for that level which is \(2k + 1\). Put it all together and multiply by every cases’ probability of \(\frac{1}{n}\) and we get the average case. See the Desmos link here for a graphical representation of this formula.

I’m not going to simply the expression but I’ll leave it as an exercise.

Also you should attempt to do find the number of failures on your own. It makes for good practice. I suggest using drawings as they are quite helpful.

Session 5 - Exam Review

We just went over problems pertaining to linked lists, stacks, and queues.

No notes this week.

Session 6 - Exam 2 Review

Sorry I haven’t been able to post any notes until now. My exams got the best of me.

The topics covered for exam 2 are:

Binary Search Trees

Binary search trees are derived from arrays of numbers an represent the different paths that the binary search can taking when looking for a

Data Structures Study Group - zac blanco