Tag Archives: script

Topological Sort

One of the things I generally say about myself is that I love learning. I can spend hours upon hours reading papers and algorithms to better understand a topic. Some of these topics are stand alone segments that I can understand in one sitting. Sometimes, however, there is a need to read up on some preliminary work in order to fully understand a concept.

Lets say that I was interested in organizing this information into a new course. The order I present these topics is very important. Knowing which topics depend on one another allows me to use the topological sorting algorithm to determine an ordering for the topics that respects the preliminary work.

The input for the topoligical sorting algorithm is a Directed Acyclic Graph (DAG). This is a set of relationships between pairs of topics, where if topic 1 must be understood before topic 2, we would add the relationship (topic 1, topic 2) to the graph. DAGs can be visualized by a set of nodes (points) representing the topics. Relationships like the one above (topic 1, topic 2) can then represented by a directed arc originating at topic 1 and flowing in the direction of topic 2. We say that the graph is “Acyclic” because there cannot be a cycle in the topic preliminaries. This amounts to us saying that a topic cannot be a prerequisite for itself. An example of a DAG is shown in the image above.

With the topics represented as a DAG, the topologial ordering algorithm works by searching the set of nodes for the one with no arcs coming into it. This node (or these nodes is multiple are present) represents the topic that can be covered next without losing understanding of the material. Such a node is guaranteed to exist by the acyclic property of the DAG. Once the node is selected, we can remove this node as well as all arcs that originate at this node from the DAG. The algorithm then repeats the procedure of searching for a nod with no arcs coming into it. This process repeats until there are no remaining nodes from which to choose.

Now lets see how the topological sort algorithm works on the graph above. We will first need to count the in-degree (the number of arcs coming into) each node.

Node | Indegree
—————-
0 | 2
1 | 2
2 | 0
3 | 2
4 | 2
5 | 2
6 | 0
7 | 2
8 | 3

Node to be removed (i.e. node with the minimum indegree): Node 2.
Arcs connected to node 2: (2, 5), (2, 3)
Resulting Indegree Count:
Node | Indegree
—————-
0 | 2
1 | 2
3 | 1
4 | 2
5 | 1
6 | 0
7 | 2
8 | 3

Node to be removed: Node 6:
Arcs connected to node 6: (6, 1), (6, 3), (6, 4), (6, 5), (6, 7), (6, 8)
Resulting Indegree Count:
Node | Indegree
—————-
0 | 2
1 | 1
3 | 0
4 | 1
5 | 0
7 | 1
8 | 2

Node to be removed: Node 3
Arcs connected to node 3: (3, 0), (3, 8)
Resulting Indegree Count:
Node | Indegree
—————-
0 | 1
1 | 1
4 | 1
5 | 0
7 | 1
8 | 1

Node to be removed: Node 5
Arcs connected to node 5: (5, 0), (5, 8)
Resulting Indegree Count:
Node | Indegree
—————-
0 | 0
1 | 1
4 | 1
7 | 1
8 | 0

Node to be removed: Node 0:
Arcs connected to node 0: (0, 1), (0, 4)
Resulting Indegree Count:
Node | Indegree
—————-
1 | 0
4 | 0
7 | 1
8 | 0

Node to be removed: Node 1
Arcs connected to node 1: none
Resulting Indegree Count:
Node | Indegree
—————-
4 | 0
7 | 1
8 | 0

Node to be removed: Node 4
Arcs connected to node 4: none
Resulting Indegree Count:
Node | Indegree
—————-
7 | 1
8 | 0

Node to be removed: Node 8
Arcs connected to node 8: (8, 7)
Resulting Indegree Count:
Node | Indegree
—————-
7 | 0

Node to be removed: Node 7
Arcs connected to node 7: none
Resulting Indegree Count:
Node | Indegree
—————-

Since there are no nodes remaining, we have arrived at a topological ordering. Going through this iteration, we can see that we arrived at the ordering (2, 6, 3, 5, 0, 1, 4, 8, 7). There were several occasions where there were multiple nodes with indegree of 0 and we could have selected an alternative node. This would have given us a different topological ordering of the nodes, but it would still be valid.

There are more learning opportunities and an interactive demonstration of the algorithm at Topological Sort Examples at LEARNINGlover.

The RSA Algorithm

I can remember back when I was in school, still deciding whether I wanted to study pure or applied mathematics. One of the common questions I would receive from those in applied mathematical realms would sound like “What’s the point of doing mathematics with no real world applications?”. Generally my response to these questions was about the intrinsic beauty of mathematics, no different from an artist painting not for some desire to be a millionaire, but because of an burning desire to paint. Whether their paintings would one day be on the walls of a Smithsonian museum or sit on their mother’s refrigerator is generally outside of the thought process of the artist. So too, would I argue about the thought process of a pure mathematician.

When I was an undergrad and learned about the RSA algorithm (named for Ron Rivest, Adi Shamir, and Leonard Adleman who discovered the algorithm) it helped me explain this concept a lot better. The algorithm is based on prime numbers and the problem of finding the divisors of a given number. Many mathematicians throughout the ages have written papers on the beauty of prime numbers (see Euclid, Eratosthenes, Fermat, Goldbach, etc). For a large period in time one of the beautiful things about prime numbers was that they were so interesting in themselves. There were questions about how to check if a number is prime, question of patterns in primes, famous conjectures like the Goldbach conjecture and the twin prime conjecture, quick ways of finding prime numbers or numbers that are almost always prime, etc. In short, this was an active area of research that much of the applied world was not using. This all changed in 1977 when Rivest, Shamir and Adleman published the RSA algorithm.

The algorithm is in the area called public key cryptography. These algorithms differ from many of the previous cryptography algorithms, namely symmetric key cryptography. Whereas symmetric key cryptography depends uses the same device (key) to encode as to decode, public key cryptography creates two keys – one for encoding that is generally shared with others, and another for decoding which is kept private. These two keys in generally relate to a mathematical problem that is very difficult to solve.

In my example script for the RSA Algorithm, I show two people who want to communicate, Alice and Bob. Bob wants people to be able to send him messages securely so he decides to use the RSA algorithm. To do this, he first needs to think of two prime numbers, p1 and p2.
From these, he computes the following:
n = p1 * p2

Next, he computes Euler’s function on this n which can be calculated as
(n) = (p1 – 1) * (p2 – 1)

Then Bob looks for a number e that is relatively prime to . This is what he will use as the encryption key.

One he has e, he can calculate d, which is the multiplicative inverse of e in (n).
This means that e * d = 1 (mod (n)).

The public key that will be used for encryption will be the pair (e, n). This is what he posts publicly via his web page for others to communicate with him securely. Bob also has a private key pair (d, n) that he will use to decrypt messages.

Alice sees Bob’s public key and would like to communicate with him. So she uses it to encode a message. The formula she uses to encrypt her message is c = me mod n, where c is the encrypted message. Once Alice encrypts her message, she sends it to Bob.

Bob receives this encoded message and uses the private key (d, n) to decode the message from Alice. The formula to decrypt is m = cd mod n.

For a more illustrative idea of how this algorithm works as well as examples, be sure to visit Script for the RSA Algorithm.

The Depth-First-Search Algorithm

I remember when I was younger I used to play the game of hide-and-seek a lot. This is a game where a group of people (at least two) separate into a group of hiders and a group of seekers. The most common version of this that I’ve seen is having one person as the seeker and everyone as hiders. Initially, the seeker(s) is given a number to count towards and close their eyes while counting. The hiders then search for places to hide from the seeker. Once the seeker is finished counting, their job is to find where everyone is hiding or admitting that they cannot find all the seekers. Any seekers not found are said to have won, and seekers that are found are said to have lost.

I played this game a number of times in my childhood, but I remember playing it with a friend named Dennis in particular. Dennis had a certain way he played as seeker. While many of us would simply go to places we deemed as “likely” hiding spots in a somewhat random order, Dennis would always begin by looking in one area of the room, making sure that he had searched through every area connected to that area before going to a new area. He continued this process until he either found everybody or concluded that he had searched every spot he could think of and gave up.

It wasn’t until years later that I was able to note the similarity between Dennis’s way of playing hide-and-seek and the Depth-First-Search algorithm. The Depth-First-Search Algorithm is a way of exploring all the nodes in a graph. Similar to hide-and-seek, one could choose to do this in a number of different ways. Depth-First-Search does this by beginning at some node, looking first at one of the neighbors of that node, then looking at one of the neighbors of this new node. If the new node does not have any new neighbors, then the algorithm goes to the previous node, looks at the next neighbor of this node and continues from there. Initially all nodes are “unmarked” and the algorithm proceeds by marking nodes as being in one of three states: visited nodes are marked as “visited”; nodes that we’ve marked to visit, but have not visited yet are marked “to-visit”; and unmarked nodes that have not been marked or visited are “unvisited”.

Consider a bedroom with the following possible hiding locations: (1) Under Bed, (2) Behind Cabinet, (3) In Closet, (4) Under Clothes, (5) Behind Curtains, (6) Behind Bookshelf, and (7) Under Desk. We can visualize how the bedroom is arranged as a graph and then use a Breadth First Search algorithm to show how Brent would search the room. Consider the following bedroom arrangement, where we have replaced the names of each item by the number corresponding to that item. Node (0) corresponds to the door, which is where Dennis stands and counts while others hide.

Bedroom Items as a Graph

Now consider how a Breadth First Search would be run on this graph.

Bedroom Items as a Graph Colored by DFS

The colors correspond to the order in which nodes are visited in Depth-First-Search.

The way we read this is that initially Dennis would start at node 0, which is colored in Blue.
While Dennis is at node 0, she notices that nodes 1, 5, and 6 (under bed, behind curtains, and behind bookshelf) are the nearby and have not been checked yet so she places them on the “to visit” list.
Next, Dennis will begin to visit each node on the “to visit” list, and when a node is visited, she labels it as visited. At each location, she also takes note of the other locations she can reach from this location. Below is the order of nodes Dennis visits and how he discovers new locations to visit.

Order Visited Node Queue Adding Distance From Node 0
1 0 6,5,1 0
2 6 5,1 7,3,2 1
3 7 3,2,5,1 2
4 3 2,5,1 2
5 2 5,1 4 2
6 4 5,1 3
7 5 1 1
8 1 1

Here is a link to my Examples page that implements the Depth-First-Search Algorithm on Arbitrary Graphs.

The Breadth-First-Search Algorithm

I remember when I was younger I used to play the game of hide-and-seek a lot. This is a game where a group of people (at least two) separate into a group of hiders and a group of seekers. The most common version of this that I’ve seen is having one person as the seeker and everyone as hiders. Initially, the seeker(s) is given a number to count towards and close their eyes while counting. The hiders then search for places to hide from the seeker. Once the seeker is finished counting, their job is to find where everyone is hiding or admitting that they cannot find all the seekers. Any seekers not found are said to have won, and seekers that are found are said to have lost.

I played this game a number of times in my childhood, but I remember playing it with a friend named Brenda in particular. Brenda had a certain way she played as seeker. While many of us would simply go to places we deemed as “likely” hiding spots in a somewhat random order, Brenda would always take a survey of the room, and no matter where she began searching, she would always make note of the locations close to her starting point and make sure she was able to give them all a look before she looked at locations that were close to the points she deemed close to the starting point. She continued this process until she either found everybody or concluded that she had searched every spot she could think of and gave up.

It wasn’t until years later that I was able to note the similarity between Brenda’s way of playing hide-and-seek and the Breadth-First-Search algorithm. The Breadth-First-Search algorithm is a way of exploring all the nodes in a graph. Similarly to hide-and-seek, one could choose to do this in a number of different ways. Breadth-First-Search does this by beginning at some node, looking first at each of the neighbors of the starting node, then looking at each of the neighbors of the neighbors of the starting node, continuing this process until there are no remaining nodes to visit. Initially all nodes are “unmarked” and the algorithm proceeds by marking nodes as being in one of three stages: visited nodes are marked as “visited”; nodes that we’ve marked to visit, but have not visited yet are marked “to-visit”; and unmakred nodes that have not been marked are “unvisited”.

Consider a bedroom with the following possible hiding locations: (1) Under Bed, (2) Behind Cabinet, (3) In Closet, (4) Under Clothes, (5) Behind Curtains, (6) Behind Bookshelf, and (7) Under Desk. We can visualize how the bedroom is arranged as a graph and then use a Breadth First Search algorithm to show how Brenda would search the room. Consider the following bedroom arrangement, where we have replaced the names of each item by the number corresponding to that item. Node (0) corresponds to the door, which is where Brenda stands and counts while others hide.

Bedroom Items as a Graph

Now consider how a Breadth First Search would be run on this graph.

Bedroom Items as a Graph

The colors correspond to the order in which nodes are visited in Breadth-First-Search.

The way we read this is that initially Brenda would start at node 0, which is colored in Blue.
While Brenda is at node 0, she notices that nodes 1, 5, and 6 (under bed, behind curtains, and behind bookshelf) are the nearby and have not been checked yet so she places them on the “to visit” list.
Next, Brenda will begin to visit each node on the “to visit” list, and when a node is visited, she labels it as visited. At each location, she also takes note of the other locations she can reach from this location. Below is the order of nodes Brenda visits and how she discovers new locations to visit.

Order Visited Node Queue Adding Distance From Node 0
1 0 1, 5, 6 0
2 1 5, 6 2, 4 1
3 5 6, 2, 4 1
4 6 2, 4 3, 7 1
5 2 4, 3, 7 2
6 4 3, 7 2
7 3 7 2
8 7 2

Here is a link to my Examples page that implements the Breadth-First-Search Algorithm on Arbitrary Graphs.

Unidirectional TSP Puzzles

As we’ve entered the late spring into early summer season, I’ve found myself wanting to go out more to sit and enjoy the weather. One of these days recently I sat in the park with a good book. On this occurrence, I decided not to go with a novel as I had just finished “Incarceron“, “The Archer’s Tale“, and “14 Stones” – all of which were good reads, but I felt like taking a break from the novels.

Just as a side note, 14 Stones is a free book available on smashwords.com and I’ve now read about 6 books from smashwords.com and haven’t been disappointed yet. My favorite is still probably “The Hero’s Chamber” because of the imagery of the book, but there are some well written ebooks available there by some good up and coming writers for a reasonable price, with some being free.

 

So with the desire to read, but not being in the mood for novels I decided to pick up one of my non-text but still educational books that make me think. This day it was “Programming Challenges“. I browsed through the book until I found one that I could lay back, look at the water, and think about how to solve it.

 

The programming puzzle the peaked my interest was called “Unidirectional TSP”. We are given a grid with m rows and n columns, with each cell showing the cost of using that cell. The user is allowed to begin in any cell in the first column and is asked to reach any cell in the last column using some minimum cost path. There is an additional constraint that once a cell is selected in a column, a cell in the next column can only be chosen from the row directly above, the same row, or the row directly below. There is a javascript version of this puzzle available here.

Fundamentally, the problem is asking for a path of shortest length. Many shortest length problems have a greedy structure, but this one gained my interest because the greedy solution is not always optimal in this case. So I took a moment to figure out the strategy behind these problems. Once I had that solution, I decided that it would be a good program to write up as a puzzle.

 

In this puzzle version, users will click the cells they wish to travel in each column in which case they will turn green (clicking again will turn them white again). Once the user clicks on a cell in the last column, they will be notified of whether or not they have chosen the minimum path. Or if users are unable to solve a puzzle, the “Solution” button can be pressed to show the optimal path and its cost. 

Learn About Descriptive Statistics

I had been meaning to write a script and blog post on descriptive statistics for some time now, but with work and winter weather and the extra work that winter weather brings, and now that the winter weather is over trying to get back into an exercise routine (running up a hill is such a challenging experience, but when I get to the top of that hill I feel like Rocky Balboa on the steps at the steps at the entrance of the Philadelphia Museum of Art), I haven’t had the time to devote to this site that I would have liked. Well, that’s not entirely true. I have still been programming in my spare time. I just haven’t been able to share it here. I went to a conference in February and in my down time, I was able to write a script on descriptive statistics that I think gives a nice introduction to the area.

Before I go into descriptive statistics though, lets talk about statistics, which is concerned with the collection, analysis, interpretation and presentation of data. Statistics can generally be broken down into two categories, descriptive statistics and infernalinferential statistics, depending on what we would like to do with that data. When we are concerned with visualizing and summarizing the given data, descriptive statistics gives methods to operate on this data set. On the other hand, if we wish to draw conclusions about a larger population from our sample, then we would use methods from inferential statistics.

In the script on descriptive statistics I’ve written, I consider three different types of summaries for descriptive statistics:

Measures of Central Tendency
Mean – the arithmetic average of a set of values
Median – the middle number in a set of values
Mode – the most used number in a set of values

Dispersion
Maximum – the largest value in the data set
Minimum – the smallest value in the data set
Standard Deviation – the amount of variation in a set of data values
Variance – how far a set of numbers is spread out

Shape
Kurtosis – how peaked or flat a data set is
Skewness – how symmetric a data set is

Plots
Histogram Plots – a bar diagram where the horizontal axis shows different categories of values, and the height of each bar is related to the number of observations in the corresponding category.
Box and Whisker Plots – A box-and-whisker plot for a list of numbers consists of a rectangle whose left edge is at the first quartile of the data and whose right edge is at the third quartile, with a left whisker sticking out to the smallest value, and a right whisker sticking out to the largest value.
Stem and Leaf Plots – A stem and leaf plot illustrates the distribution of a group of numbers by arranging the numbers in categories based on the first digit.

Slope Formula

I was watching a football game a few days ago, and to prepare for it, we decided to pick up some snacks. As the game progressed, I found myself eating a lot of chips, but at halftime, there was one snack that remained unopened, a snack I had been thinking about all night, a snack that I couldn’t seem to locate until that moment – Recee’s Pieces. We purchased a pretty large sized bag that I thought would last a while. So at the end of halftime, when the bag was almost empty, someone made sure to warn me that at the rate I’m eating these things I’d be sure to be sick the next day.

Just to give you some insight into my how my mind works, the fact that she used the term “rate” took me back to classes of Algebra 1 where we were first learning about linear equations, slopes, y-intercepts, point slope form, slope intercept form, and so on. The more I thought about it, the more I thought this would be a good script to add to this site as it would probably provide help to many students who are currently enrolled in those classes as well as a remembrance to adults who took those classes years ago and wish to recall the concepts.

So I have added a script which helps go over the slope formula. It randomly generates two points and asks users to select which of a set of choices is the slope of those two numbers. In case you forget, there is a button where you can be given the slope as well.

Interactive Midpoint Formula

Interactive Midpoint Formula

I hope everyone had a great time over the holidays reconnecting with their family and friends. I definitely enjoyed hearing about the changes in their lives since we last spoke, and meeting new additions to the families. One of the best times I had was with a two year old who I had met earlier in the year. She was much more responsive this time, and as we sat and talked, she was eager to tell me the things she knew, from the alphabet to her numbers, to her shapes. So today’s blog-script is inspired by this conversation, which became a game of point and describe. I would point to an object and she would say “That’s a circle”, or “That’s a red triangle”. I was amazed at both how simple it was and how much I enjoyed this activity.

I enjoyed it so much that I decided that I’d like to have some programs on my site that were more of this genre. The first that I decided to write is a lesson on the midpoint formula. But instead of simply giving the formula and writing a script to walk users through the steps in calculating the midpoint, I thought I’d write a point-and-click approach to it.

The script will randomly generate two points in the XY plane and ask users to calculate the midpoint between these points. Five options are then given and the user is asked to select the radio button next to the correct choice. Once the submit button is pressed, the program will let users know if their choice is correct. Users can have the program generate new points at any time. There is also an option for users to have the midpoint formula displayed.

Because this is my first program of this sort, I am curious to know what users think. When generating choices that are supposed to be incorrect, what’s a good method for doing so? I decided not to keep a timer or a score for the “score” of a user, but I did think about it. Ultimately I wanted this to feel less like a “test” and more like a “game” so I decided against this option. But I would like to know what you think – either through your comments here or on twitter @MindAfterMath.

Once again, here’s the link to the script. I’ll keep you updated on if my new two year old friend likes this.

Triangle Sum Puzzle

This is probably a consequence of being a mathematician, but I have always enjoyed number puzzles. I think that there is a general simplicity and universality in numbers that are not present in things like word puzzles, where the ability to reach a solution can be limited to the vocabulary of the user.

The fact that these are puzzles and not simply homework exercises also helps because we often find people sharing difficulties and successes stories over the water cooler or at the lunch table. The fact that many of these math puzzles can teach some of the same concepts as homework problems (in a more fun and inclusive way) is generally lost on the user as their primary interest is generally on solving the puzzle in front of them, or sometimes solving the more general form of the puzzle.

Today’s post is about a puzzle that was originally shared with me over a lunch table by a friend who thought it was an interesting problem and asked what I thought about it. I didn’t give the puzzle much further thought (he had correctly solved the puzzle) until I saw it again in “Algorithmic Puzzles” by Anany Levitin and Maria Levitin. It was then that I thought about the more general form of the puzzle, derived a solution for the problem, and decided to code it up as a script for my site.

Below is a link to the puzzle:

We have a set of random numbers arranged in a triangle and the question is to find the path of maximum sum from the root node (the top node) to the base (one of the nodes on the bottom row) with the rules that
(1) Exactly one number must be selected from each row
(2) A number can only be selected from a row if (a) it is the root node or (b) one of the two nodes above it has been selected.

For the sample
So for the sample problem in the picture, the maximal path would go through nodes 57, 99, 34, 95, and 27.

For more of these puzzles check out the script I write here and be sure to let me know what you think.

The Bridge Crossing Problem

Most puzzles are fun in their own right. Some puzzles are so fun that they have the added benefit that they are likely to come up in unexpected places, like maybe in a job interview. I was recently reading a paper by Günter Rote entitled “Crossing the Bridge at Night” where Rote analyzes such a puzzle. Upon finishing the paper, I decided to write a script so that users could see the general form of this puzzle.

The problem can be stated as follows: There is a set of people, lets make the set finite by saying that there are exactly n people, who wish to cross a bridge at night. There are a few restrictions that make crossing this bridge somewhat complicated.

  • Each person has a travel time across the bridge.
  • No more than two people can cross the bridge at one time.
  • If two people are on the bridge together, they must travel at the pace of the slower person.
  • There is only one flashlight and no party (of one or two people) can travel across the bridge without the flashlight.
  • The flashlight cannot be thrown across the bridge, and nobody can go to the store to purchase another flashlight

The image above shows the optimal solution when the 4 people have travel times of 1, 2, 5, and 10. The script I have written allows users to work with different numbers of people with random travel times. Give it a try and see if you can spot the patterns in the solution.