Just Enough Set Theory – When Sets Collide (Part 3 of 3)

Welcome to the final installment of this three-part series on set theory. The first piece, Set Theory Defined, detailed requisite foundational knowledge. The second article, Set Operations, outlined some beneficial set algorithms. This post develops the concepts laid out in the first two; therefore, it is highly recommended that readers begin there.

Individual sets have many useful properties; however, preforming operations on multiple sets provides even greater utility. This piece outlines four such operations. Each operation provides a concise means for addressing common programming problems that virtually all software professionals encounter. There is a brief description of each from a mathematical perspective followed by JavaScript (ES6) code excerpts demonstrating how to apply theory to real world scenarios.

NOTE: All code samples are written in ES6 and are therefore not likely to execute directly in a browser. The best option is to use Node or transpile the excerpts using either Babel or TypeScript. The working code is available on GitHub along with execution instructions.


The union of two sets is a set containing the distinct elements from both sets. `\cup` is the mathematical symbol for a union and the union of sets `A` and `B` is denoted as `A \cup B`. An expanded way of representing the union relationship is `\{x| x \in A \vee x \in B\}`, which means every element contained in `A` OR (`\vee`) `B`. Figure One – Union depicts two sets with three elements each. The union is a set with five elements because one item, three, is shared and union returns distinct values. The Venn diagram shows the relationship graphically.


Generating the union of two sets is quite easy in ES6 as the code below illustrates.

const A = new Set([1, 2, 3]);
const B = new Set([3, 4, 5]);
const union = new Set([...A, ...B]);
// union = [1,2,3,4,5];

The astute reader will notice that there’s some legerdemain afoot. The code above uses the ES6 Set data structure instead of standard JavaScript arrays. Set holds only unique elements by ignoring add operations for new values that match existing ones. The algorithm is as easy as concatenating the two sets without the concern of distinct elements. If the code was using standard arrays, there would have to be logic to remove duplicated items. Luckily, converting between sets and arrays is virtually effortless.

const setDataStructure = new Set([1, 2, 3]);
const arrayDataStrcture = Array.from(setDataStructure);

The problem with the code above is that it’s a rare requirement to union sets containing primitive values. Software engineering is seldom that straightforward. A more realistic scenario is calculating the union between two sets of complex objects where equality becomes problematic. Unlike primitive variables, objects with identical values are not equal because they compare by reference. This abrogates the Set trick from earlier. Suppose the requirement is to compute all bug reports currently in process across two teams and it’s possible that both teams are working on the same bugs simultaneously. The code below demonstrates a solution by first concatenating the two sets and then removing duplicates using the filter method introduced in the last article. Notice the only equality check is via the Id. Obviously, this won’t work for every scenario and depending on the size of the sets and performance requirements it is possible to write generic deep equality methods (or use a library like underscore).

const teamABugs = [
    { id: 1, name: "Screen Explodes" },
    { id: 2, name: "Keyboard Burts into Flames" },
    { id: 3, name: "Submit button off by 1 pixel" }];
const teamBBugs = [
    { id: 5, name: "Randomly Dials Russian Hackers" },
    { id: 6, name: "Publishes CC info to the www" },
    { id: 3, name: "Submit button off by 1 pixel" }];

const union = [...teamABugs, ...teamBBugs]
    .filter((x, index, array) => array.findIndex(y => y.id == x.id) == index);


The intersection of two sets is a set containing distinct shared elements. `A \cap B` is the mathematical representation of a union and the expanded notation is `\{x|x \in A \wedge x \in B \}`. Stated differently, the intersection of set `A` AND (`\wedge`) `B` is every element contained in `A` AND `B`. Figure Two – Intersection depicts the relationship showing the union of `A` and `B` to be a singleton set containing only the number three. Once again, the Venn diagram portrays the relationship.


Much like union, finding the intersection of two sets using the Set data structure and primitive types is easy. The code below shows how it’s a matter of using the filter method to check to see if an item is also stored in the other set.

const A = new Set([1, 2, 3]);
const B = new Set([3, 4, 5]);
const intersect = [...A].filter(x => B.has(x));
// intersect = [3];

The code above is a bit fanciful. Consider instead a role protected resource. Possessing any one of many roles allows users to access said resource. Users each have a set of associated roles. There are a few different ways to achieve this, but finding the intersection between the user’s roles and the resource’s required roles is the most manageable. See the code below.

const resourceRoles = [
    { id: 1, name: "Administrator" },
    { id: 2, name: "Super User" }];
const user =  { id: 314, name: "Edsger Dijkstra", roles: [
    { id: 1, name: "Administrator" }, 
    { id: 2, name: "User" }] }

const hasAccess = resourceRoles
    .filter(x => user.roles.find(y => y.name == x.name)).length > 0;

All of the caveats about equality described in the Union section also apply here. It’s something programmers need to be cognizant of.


The difference of two sets is sometimes known as the relative complement; both nomenclatures are interchangeable. The concept is simple, the difference is a set made up of the items that are left over after removing the intersection of another set. Otherwise stated, all of the items in set `B` that do not exist in set `A`. Mathematically, this is represented as `\{x|x \in B \wedge x \notin A\}` or the shortened version which is `B\\A`. Figure Three – Difference shows the difference between `B` and `A` to be a set containing four and five. Just as above, there is a representative Venn diagram.


As an aside, there is also an absolute compliment which is somewhat similar; however, it is outside the scope of this article.

Finding the difference of sets is almost identical to finding the intersection as the code below demonstrates. The only variation is that the predicate passed to the filter method is negated.

const A = new Set([1, 2, 3]);
const B = new Set([3, 4, 5]);
const difference = [...B].filter(x => !A.has(x));
// difference = [4,5];

Again, a more realistic example is in order. Image that there is a set of actions that must be completed and a set of actions a user has completed. Finding the difference is an easy way to determine if all required actions are complete.

const requiredActions = [
    { id: 1, name: "Electronic Signing" },
    { id: 2, name: "Submission Form" },
    { id: 3, name: "Payment" }];
const userActions = [
    { id: 1, name: "Electronic Signing" },
    { id: 2, name: "Submission Form" }];

const complete = requiredActions
    .filter(x => !userActions.find(y => y.name == x.name)).length === 0;
// complete = false

Cartesian Product

The Cartesian product of two sets is a set of ordered pairs that contain all possible combinations of elements in the two sets. The mathematical representation is `A \times B`. The expanded notation is `\{(a,b)|a \in A \wedge b \in B\}` which means an ordered pair consisting of every element in `A` AND (`\wedge`) every element in `B`. Figure Four – Cartesian Product demonstrates the concept. As a matter of importance, unlike standard products, the Cartesian product is not commutative. Stated mathematically, `A \times B \ne B \times A`. Switching the order of statement will change the order of the pairs.


The Cartesian product is useful for combinatorics problems. A common example is simulating a deck of cards. Instead of specifying all the cards explicitly in code, it’s easier to define the suits and values as two separate sets and then take the Cartesian product to get the entire deck. See the code below.

const suits = ['Diamond', 'Spade', 'Heart', 'Club'];
const values = ['Ace', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'Jack', 'Queen', 'King'];

const cards = suits.reduce((acc, x) => [...acc, ...values.map(y => [x, y])], []);
// Alternatively, it’s possible to return the ordered pair as an object instead of an array
// const cards = suits.reduce((acc, x) => [...acc, ...values.map(y => { return { suit: x, value: y } })], []);

This code should be starting to look familiar because all the samples make heavy use of the map, reduce, and filter methods. Using ES6, these methods have great utility for mimicking mathematical set operations. Because the code above is similar to previous examples, it doesn’t require further explanation.

Why Stop at Two?

Up to this point, all the exhibited set operations employ two sets. However, this is for the sake of brevity. Each operations can act on as many sets as required. For instance, `A \cup B \cup C` is perfectly valid as is `A \times B \times C`. The enthused reader should solidify his/her learning by expanding each code sample to use additional sets.

Real World Applications

This series demonstrated how set theory is applied to data structures and demonstrated some novel uses for set operations in order create efficient algorithms. However, this is only a meager representation of all the many and varied applications for software engineering. Relational databases make heavy use of set theory for defining data structure and constructing data queries. In fact, SQL is essentially a set notation. There are several instances in language theory and design where strings are realized as sets and set operations are performed on them. Another prolific use is in computer graphics where points on a plane are treated as sets. The list of applications is considerable. It’s a body of knowledge that no software professional should forsake.


Thus concludes this three-part series on set theory. Hopefully, the reader has gained a high-level understanding as well as enough practical knowledge to apply the learning forthwith. The first article outlined the basics and introduced the concept of set mapping. Empty sets, cardinality, subsets, summation, and power sets were introduced in the second piece. Finally, this post presented operations involving more than one set including unions, intersections, differences, and Cartesian products. The method was to first introduce the ideas mathematically and then demonstrate how to apply them using ES6. These concepts should not be considered optional for software professionals because set theory is ubiquitous in computer science.

As always, thank you for reading and please feel free to contact me with questions. I’m also happy to create more in depth posts upon request.

Just Enough Set Theory – Set Operations (Part 2 of 3)

Welcome to the second installment of this three-part series on set theory. The first piece, Set Theory Defined (recently updated with code samples), detailed requisite foundational knowledge. It is highly recommended that readers begin there if they haven’t already.

The first piece in this series introduced sets and exhibited how ES6 arrays are analogous to them. It also depicted how to transform, or map, a set into a related set. This post expands on set theory by probing into set operations.

NOTE: All code samples are written in ES6 and are therefore not likely to execute directly in a browser. The best option is to use Node or transpile the excerpts using either Babel or TypeScript. The working code is available on GitHub along with execution instructions.

Empty Sets

Empty sets are a rather mundane topic, but nonetheless worth mentioning. As the name implies, they are simply sets that have no elements. They are also commonly referred to as null sets. Mathematically, empty sets are represented as either `\emptyset` or `{}`. The concept relates to empty arrays in software.


The term cardinality sounds impressive; however, it’s simply the number of elements in a set. The mathematical representation of a set with three elements is as depicted in Figure One – Cardinality.


In JavaScript, the cardinality of an array is its length. See the code below.

const someSet = [1, 2, 3, 4, 5]; 
const cardinality = someSet.length; 
// cardinality = 5


Subsets are relatively easy to explain, yet have far reaching implications. A subset is a portion of a larger set. For instance, consider the set of all animals (`A` ). The set of all dogs (`D` ) is a subset of the animal set because although every animal is not a dog, every dog is an animal. The mathematical notation for subsets is as follows: `D\subseteqA`. Another way of mathematically expressing the subset relationship is `\forall x(x\inD->x\inA)`. That looks absurd, but the premise is that for any (`\forall`) element (`x`) in `D` , it is implied (`->`) that the element (`x`) also exists in `A`.


Subsets are often taught with Venn Diagrams. See Figure Three – Venn Diagrams for an example. Admittedly, this account of subsets is a bit prosaic. However, the final post in this series relies heavily on the concept so it bears belaboring the point.


ES6 has a built-in filter method on the array object that enables easy access to subsets. Filter takes a predicate as an argument. Recall from the first article that a predicate is a function that takes a single argument and returns a Boolean response. The filter method applies the predicate to each item in a set and creates a new set that includes the items where the predicate returned true. See the code below.

const animals = [
    {name: "Tom", type: "Cat"},
    {name: "Jerry", type: "Mouse"},
    {name: "Pluto", type: "Dog"},
    {name: "Scooby Doo", type: "Dog"}];

const dogs = animals.filter(a => a.type == "Dog");
// dogs = [{name: "Pluto", type: "Dog"}, {name: "Scooby Doo", type: "Dog"}]


The term summation is a bit misleading because it implies simply adding elements together, however it’s a more powerful concept. Summation applies a function to each element of a set reducing it to a single value. `‎\sum_{x \in S}f(x)‎‎` is the mathematical notation representing the algorithm where `S` can be any set and `f(x)` can be any function. Consider Figure Four – Summation. Given the set `A`, each element in the set is multiplied by two and added together.


ES6’s reduce method of the array object is comparable to summation. Aptly named, reduce applies a function to each member of a set reducing it to a single value. It accepts two arguments: a function and an optional starting value. The function accepts an accumulated value and the current item. The state of the accumulated value after all items are processed is the final return value. The code below is the same process detailed in Figure Four – Summation.

const someSet = [1, 2, 3];
const sum = someSet.reduce((acc, x) => acc + x * 2, 0);
// sum = 12

Reduce is useful for many operations beyond mathematical functions. The code below utilizes it to extract email addresses from a set of users.

const users = [
    {id: 1, email: "email@email.com"},
    {id: 2, email: "email2@email2.com"},
    {id: 3, email: "email3@email.com"}];

const emails = users.map(u => u.email).reduce((acc, x) => `${acc};${x}`);
// emails = "email@email.com;email2@email2.com;email3@email.com"

This above doesn’t do the reduce method proper justice because its efficacy is virtually endless. There are many more options that are outside the scope of this feature. The reader is highly encouraged to find more information on Mozilla’s excellent JavaScript reference.

Power Set

Power sets are something every programmer has to deal with at some point in his/her career, even if they can’t formally identify them by name. In mathematical parlance, power sets are denoted as `P(A)`. A power set is the set of all subsets including the empty set and itself: more succinctly, all possible set combinations. A power set always contains `2^n` elements where `n` is the cardinality of the original set (`|P(A)|=2^(|A|)`).

Power sets are difficult to conceptualize without an example. Figure Five – Power Set depicts a set with three elements. The power set is all possible combinations of the three elements. The result is a set with a cardinality of eight (`2^3`).


Unfortunately, there isn’t an innate JavaScript method for creating power sets. However, that’s an easy problem to overcome given some ingenuity. See the code below.

const someSet = [0, 1, 2];
const powerSet = someSet.reduce((acc, x) => [...acc, ...acc.map(y => [x, ...y])], [[]]);
// powerSet = [[], [0], [1], [1,0], [2], [2,0], [2,1], [2,1,0]]

The code above is a bit intimidating at first glance so it merits additional explanation. The power set always contains an empty set, so the second argument to the reduce method is a set that contains nothing but that. This is the starting value. When the function acts on the first item in the set, the value of acc is [[]] and the value of x is 0. The result of concatenating the current item to each item in acc is concatenated on to the value of acc making it [[], [0]]. The same algorithm is applied to each item in the set. This is difficult to envisage, so the code below details essentially what happens upon invocation.

const ps = (acc, x) => [...acc, ...acc.map(y => [x, ...y])]; 

// First element
let acc = ps([[]], 0);
// acc = [[], [0]]

// Second element
acc = ps(acc, 1);
// acc = [[], [0], [1], [1,0]]

// Third element
acc = ps(acc, 2);
// acc = [[], [0], [1], [1, 0], [2], [2, 0], [2, 1], [2, 1, 0]]

The reader is highly encouraged to review this section multiple times until the concept solidifies.


The post outlined a few useful set operations. ES6 uses the reduce method to apply the concept of summation to sets. A power set is a set of all possible set combinations. Although there is no built in ES6 functionality for this, it’s an easy algorithm to create. Make sure to come back for the final post entitled When Sets Collide. It is by far the most useful in the series covering set operations that act on multiple individual sets.

Just Enough Set Theory – Set Theory Defined (Part 1 of 3)

Set theory is incredibly intuitive and has many practical applications in software engineering. In f

Set theory is incredibly intuitive and has many practical applications in software engineering. In fact, any professional programmer without an understanding is at a disadvantage. Unfortunately, many in the industry relegate it to the purview of mathematicians. This is understandable because most material on the subject delineates set theory with first order logic as a basis for math. The good news is that it doesn’t have to be this way. As this series demonstrates, it is accessible to anyone regardless of background.

The three articles in this series aim to introduce set theory, expound upon set operations, and demonstrate the learning using JavaScript (ES6). The goal is to provide the reader with actionable knowledge to improve his/her software skills without a surfeit of superfluous details. This first installment describes the theory in order to provide a firm foundation for future practical application.

NOTE: All code samples are written in ES6 and are therefore not likely to execute directly in a browser. The best option is to use Node or transpile the excerpts using either Babel or TypeScript. The working code is available on GitHub along with execution instructions.

What is Set Theory

The inception of set theory dates back to the nineteenth century with Georg Cantor. On the surface, it’s brilliantly simple. A set is simply a collection of unordered objects. In mathematical parlance, objects contained in a set are known as members or elements. An element can be literally anything, including another set. Sets are typically depicted as objects inside curly braces and are denoted by capital letters. For instance, `A= { 1,2,3 }` is the mathematical representation of the set `A` with the members `1`, `2`, and `3`. Set membership is signified as: `1\inA`. Figure One – Sets illustrates these symbols.


Set theory relies on FOPL (First Order Predicate Logic) to construct sets. Expanding on the definition above, sets are a collection of objects that satisfy a predicate. A predicate is a function that accepts a single argument and returns a Boolean (true or false) value. For instance, the set of all dogs has the predicate `IsDog(n)` In other words, elements of a set share some arbitrary property. FOPL is fascinating, but not particularly relevant to this article. A general acumen of predicates is sufficient for comprehension of this material. A cursory web search for First Order Logic will present sufficient resources for the curious reader.

Set Mapping

There are a few interesting operations that can be performed on sets, most of which are covered in the next installment. However, mapping from one set to another is germane to a foundational understanding of set theory. A set is transformed, or mapped, into another related set via the use of a function.

A mathematical function is analogous to a software function with added constraints. They are similar in that they accept an input and return an output. The difference is that a mathematical function can only accept a single input, must return an output, are determinate, and side effects are impermissible. Sources often refer to functions as relations between sets because they map a member of a set to member of another set. While mathematical functions are relevant to the understanding of set theory, programmers need not be particularly concerned with this concept. The significant notion is that of a function in general, which should be apparent to most software professionals. As an aside, further understanding of mathematical functions is particularly useful for other programming concepts.

Mapping works by applying a function to each member of a set and placing the output into another set. Figure Two – Set Mapping illustrates the concept. This is particularly applicable to programming, so understanding is imperative.


Given the information above, the impetus of the map method of arrays in JavaScript (ES6) is obvious. Arrays are a convenient analog to sets. See the code sample below.

const wholeNumbers = [1, 2, 3];

const evenNumbers = wholeNumbers.map(n => n * 2);
// evenNumbers = [2, 4, 6]

The above isn’t exactly a realistic scenario: generating an array of doubled numbers isn’t auspicious. A more real world use of the map method is to modify complex objects. See the code below.

const people = [{id: 1, name: "Ada Lovelace"}, {id:2, name: "Charles Babbage"}];

const names = people.map(p => p.name);
// names = ["Ada Lovelace", "Charles Babbage"]

Map is exceedingly suitable for many use cases. Understanding set theory elucidates its utility.


As a fair warning, the remainder of this post provides a prospectus of the areas of set theory that aren’t directly applicable to everyday programming activities. Although intriguing, the uninterested reader should feel free to skip to the conclusion.

To Infinity and Beyond

The conception of sets isn’t exactly revolutionary. Kindergarten pedagogy teaches children to categorize objects into sets. It’s simple and intuitive. The innovation is revealed by examining sets of infinite size.

Conceptually, there are two methods for comparing the sizes of sets. The first is to enumerate the members and compare the resulting counts. This is blindingly obvious; however, it has a substantial flaw. It isn’t possible to calculate the number of members in an infinite set. As a second option, Cantor postulated that if it is possible to create a function that maps the first set to the second set without skipping members, then the sets must be of equal size.

The canonical example is to compare the set of natural numbers (whole numbers excluding zero) to the set of even natural numbers. Figure Three – Counting Sets demonstrates the concept. Although it’s not exactly intuitive, and is often controversial, this establishes that the two infinite sets are equally sized. This might lead one to believe infinity is simply infinity. However, it’s a bit more abstruse.


Consider the set of real numbers (natural, rational, irrational, and transcendental) between one and two. Think back to the number lines that are an inexorable part of preparatory education and envision a set encompassing all numbers on the line between one and two. Regardless of the placement of two distinct points on the line, it is possible to find a smaller number between them. The interesting thing about this infinite set is that it is not possible to create a function that maps the set of natural numbers to this set without skipping members. This implies that although both sets are infinite, the set of real numbers between one and two is actually larger than the set of all natural numbers. Cantor verified this in a beautifully elegant proof known as Cantors Diagonalization.

While theoretically straightforward, the notion of multiple sizes of infinity is a bit vexatious. John von Neumann once said, “in mathematics you don't understand things. You just get used to them.". This concept holds true to his conjecture. The good news is that the notion of different sizes of infinity is only applicable in the most esoteric areas of computer science. The majority of programmers need not concern themselves with it.

Don’t be Naïve

Set theory took the mathematical world by storm with its simplicity and elegance. Many foundational theories are built on the cornerstone of set theory. However, it contains a substantial flaw which could have spelled doom except that mathematicians couldn’t deny its utility. Therefore, it split into two separate theories known as naïve and axiomatic set theory. It’s similar to how general and special relativity exist simultaneously.

Naïve set theory is sufficient for many applications. In fact, it is adequate for almost all software engineering use cases. Axiomatic set theory does apply to some esoteric areas of computability and logic. However, it is far removed from the greatest majority of programming tasks.

As for axiomatic set theory, it is an extension of the original theory that introduces several axioms that address flaws. The underlying issue with naïve set theory is that a paradox can arise when defining predicates. The most popular demonstration of the defect is Russell’s Paradox. Succinctly stated: does the set of all sets that do not include themselves include itself? If the answer is yes, then the definition is contradictory because it does contain itself. If the answer is no, then the predicate is likewise inconsistent because it cannot contain all sets that do not contain themselves. Don’t worry if this seems perplexing, it often requires reflection.

The finer points of axiomatic set theory are beyond the scope of this article. However, the intrigued reader should perform a web search for Zermelo–Fraenkel set theory to learn more. Regardless of its applicability to programming, it’s quite captivating.


The most pertinent programming related concepts detailed in this post are sets and set mapping. A set is simply a collection of objects. Set mapping is applying a function to each member of a set to produce a related set. The following pieces in this series expound on how these concepts are applicable.

Set theory is surprisingly simple yet it reveals some mystifying truths such as the fact that there are multiple sizes of infinity. There are essentially two branches of set theory: naïve and axiomatic. Naïve set theory is sufficient for the majority of software engineering applications.

Make sure to come back for the next article. With the foundational concepts out of the way, the post delves into set operations which provide valuable mental models for programmers. These are concepts that will improve your development abilities.

Coding Theory (Part 3 of 3) – Demonstration

Welcome to the final installment of this three-part series on coding theory. If you have not had the opportunity to read the first two pieces, it is highly recommended that you do before continuing on. They are available here:

Having covered cogent concepts in previous posts, this article aims to dive into a demonstration which consists of defining a code using a generator matrix and correcting errors using a parity check matrix. The example is a bit contrived and thoroughly simplified for the sake of brevity. However, the intent is not to provide an exhaustive resource; it is to familiarize the reader with coding theory and hopefully entice him/her into further inquiry.

As a fair warning, this post contains a modest amount of high school/first year college level math. An understanding of Boolean algebra (integer arithmetic modulo two) and matrices are a welcomed asset to readers. However, learners less accustomed to these concepts can still follow along and simply have faith that the math works out as advertised. A cursory overview of relevant math concepts is provided where appropriate.

Generator Matrix

A generator matrix is a simple, yet particularly clever means of generating codes. They are comprised of an identity matrix combined with an arbitrary matrix. Multiplying a message in row matrix form by a generator matrix produces a codeword. This is difficult concept to grasp without an example. Therefore, the remainder of this section is step-by-step instructions for creating a generator matrix that will produce a code with eight codewords.

The first step is to define an identity matrix which is a matrix that any given matrix can be multiplied by without changing the value of the given matrix. This is accomplished by setting the principal diagonal elements to one and leaving the rest as zero. See figure one for an example. The matrix is of order three because a three-digit binary string can represent eight possible values which is the number of desired codewords.


The next step is to define an arbitrary matrix (denoted by `A`). The size of the matrix determines the size of generated codewords. If `m` is the size of the identity matrix, and `n` is the desired length of codewords, then the arbitrary matrix should be of size `m-by-(n-m)`. Six digit codewords suffice for the purposes of this article; therefore, the arbitrary matrix must be sized three by three (six-digit length minus three-digit identity). Figure two is `A` as used by the remaining examples.


The only thing left do is combine the two matrices above together to form `G`. It’s as simple as placing them side by side as shown in figure three. 3-figure3

With the generator matrix (`G`) in hand, generating codewords is trivial. Multiplying any three-digit binary message in row matrix form produces a codeword. For example, the message `011` becomes the codeword `011110` as shown in figure four. Notice the codeword is the original message with three parity bits appended. This happens because the generator matrix begins with an identity matrix.


Examining the Code

The example code (`C`) is comprised of every number between `000` and `111` multiplied by the generator matrix as shown in figure 5. The example code has a couple of notable attributes. The first is that the sum of any two codewords is yet another codeword. This is known as a linear code. Another extraordinary characteristic is that the minimum hamming distance of the code is equal to the minimum weight of the nonzero codewords. Weight is the number of ones within a codeword. The reasons for this are beyond the scope of this post; it is mentioned to seduce the reader into continued exploration. Examining the code reveals that the minimum hamming distance is three (`d(C) = 3`).


With the code in hand, it’s possible to calculate the equations outlined in part two of this series. First, it’s pertinent to know how many errors the code is capable of detecting and correcting. The previous paragraph defines the minimum hamming distance as three. Figure six demonstrates that the example code is capable of detecting a maximum of two errors and correcting a maximum of one.


Another relevant equation introduced in the second installment of this series is the Hamming bound. Recall that the `|C|` denotes the upper bound number of codewords, `n` is the length of the codewords, and `k` is the maximum number of errors the code is capable of correcting. Figure seven demonstrates plugging these variables into the Hamming bound equation.


The remainder of this post deals with detecting and correcting errors after transmission. Parity check matrices, described in the next section, are a counterpart to generator matrices that facilitate error detection and correction.

Parity Check Matrices

Parity check matrices are derived from generator matrices. They are used during the decoding process to expose and correct errors introduced during transmission. Multiplying a parity check matrix by the transpose of a codeword exposes errors. The concept is best elucidated by demonstration.

A parity check matrix (denoted as `H`) is comprised of the transpose of the arbitrary matrix combined with the identity matrix. As a refresher, the transpose of a matrix is simply the matrix flipped across it’s diagonal so that the (`i`,`j`)th element in the matrix becomes the (`j`,`i`)th element. Figure 8 shows the parity check matrix that corresponds to the generator matrix from the running example.


Multiplying the transpose of any valid codeword by the parity check matrix produces a zero-value result as demonstrated in figure nine. The mathematical rational for this this is beyond the scope of this post. However, it is a worthwhile endeavor for the reader to research this further.


Changing any of the bits in the codeword produces a non-zero result which indicates an error. Consider `011010`, as shown in figure ten. The result does not equal zero so at least one of the bits is erroneous.


After identifying an inaccurate codeword, it may be possible to correct it using `H`. Continuing with the example above; the product of the codeword and `H` is equal to the forth column in `H`. This indicates an error in the fourth bit and changing the fourth bit produces the correct codeword. See figure eleven for an illustration.


Because the example code is only capable of correcting a single error, changing more than one bit generates an irrecoverable codeword. However, with a more complex code, it is possible to correct multiple errors using the distinct sum of `H` rows and the nearest neighbor method. Again, the reader is encouraged to expand on this with more research.


This concludes the three-part series on coding theory. Coding theory is a fascinating field that enables the reliable transfer of information in spite of the shortcomings inherent in computing machinery. Richard Hamming, a pioneer in the field, devised ingenious codes that allow a maximum amount of data recovery using a minimum amount of redundancy. His codes are still widely used and have many practical applications. This post demonstrated Hamming’s methods by providing step-by-step instruction for generating codewords using a generator matrix. Additionally, it illustrated how to derive a parity check matrix from the generator matrix and use it to correct errors.

Thank you for taking the time to read this series of articles. As always, I’m happy to answer any questions or embellish details in future posts upon request. I hope this series has enthused the reader into more acute exploration.

Coding Theory (Part 2 of 3) – Perfect Error Correction


Welcome to the second installment of this three-part series on coding theory. If you have not had the opportunity to read the first piece, it is highly recommended that you do before continuing on. It is available here: http://hideoushumpbackfreak.com/post/2016/07/30/Coding-Theory-(Part-1-of-3)-Coding-Theory-Defined

It’s rare to find concepts simple yet adroit at the same time. However, Hamming’s contributions to coding theory “fits the bill”. This post begins with a brief introduction to Hamming and a short history lesson before diving into Hamming Distance, and Perfect Codes. Additionally, it delves into a few simple math concepts requisite for understanding the final post. These concepts all come together in the final installment by providing examples of how to generate and decode the most powerful and efficient error correcting codes in use today.

Richard Hamming

Richard Hamming was an American mathematician that lived from 1915 thru 1998. Early in his career, he programmed IBM calculating machines for the infamous Manhattan project. Concerned about the pernicious effect he may be having on humanity, he abandoned the Manhattan project to work for Bell Laboratories in 1946. Hamming’s tenure at Bell Laboratories was illustrious. His contributions during that time include Hamming codes, Hamming matrix, Hamming window, Hamming numbers, Hamming bound, and Hamming distance. The impact of these discoveries had irrevocable implications on the fields of computer science and telecommunications. After leaving Bell Laboratories in 1976, Hamming went into academia until his death in 1998.

The Inception of Error Correcting Codes

The world of computation was very different back in 1947. At that time, producing modest (by today’s standards) calculations could take days. Just like today, machines of yore operated on bit strings with parity bits to ensure data fidelity. However, upon detecting erroneous data, the machines had no choice but to halt computation and return an error result. Imagine the frustration of being 47 hours into a 48-hour program and having it error out due to an anomaly introduced by noise. This is the dilemma Richard Hamming faced.

In 1950, Hamming published a paper that would serve as the basis for modern coding theory. He postulated that it was possible to not only detect, but correct errors in bit strings by calculating the number of bits disparate between valid codes and the erroneous code. This came to be known as Hamming Distance.

Hamming Distance

The Hamming distance between two codewords is simply the number of bits that are disparate between two bit strings as demonstrated in figure one. Typically, hamming distance is denoted by the function `d(x,y)` where `x` and `y` are codewords. This concept seems incredibly mundane on the surface, but it’s the inception of a whole new paradigm in error correcting codes; specifically, Nearest Neighbor error correction.

2-figure1Nearest neighbor error correction involves first defining codewords, typically denoted as `C`, that are known to both the source and sink. Any received codeword not contained in `C` is obviously the result of noise. Upon identifying an erroneous codeword, nearest neighbor decoding calculates the Hamming distance between it and every codeword contained in `C`. The codeword with the smallest Hamming distance has a high probability of being correct. See figure two.

2-figure2The quality of error correction is heavily dependent on choosing efficient codewords. `d(C)` denotes Minimum Hamming Distance: that is the smallest hamming distance between any two code words contained within `C`. If a code has a minimum hamming distance of one (`d(C)=1`) then nearest neighbor error correction is futile. If it has a large hamming distance, such as 10 (`d(C)=10` ), then error correction is powerful.

Hamming represented the relationship between minimum hamming distance and the quality of error correction with two concise equations. A particular code can detect a maximum `k` errors in a codeword if `d(C) <= k + 1` and correct a maximum of `k` errors if `d(C) >= 2k + 1`. For example, a code with `d(C) = 10` can detect a maximum of nine errors and correct a maximum of four as demonstrated in figure 3.


An important fact to note is that the equations above represent the maximum bounds of error detection and correction. It is possible to create a code with a minimum hamming distance that falls short of these bounds. In reality, it’s difficult to create a code that effectuates the bounds. There are special codes, known as Perfect Codes, that meet this criterion as well as demonstrate some other desirable traits.

Perfect Codes

Generating an efficient code is a formidable task because it involves three competing principals as shown in figure four. First, short codewords reduce the size of data transmissions. Likewise, as shown in the previous section, the greater the minimum Hamming distance, the greater the codes ability to detect and correct errors. However, there are a limited number of codewords of a specified length that also have a specified minimum Hamming distance.


The Hamming Bound equation demonstrates these competing principals concisely. The equation is shown in figure five, where `|C|` is the upper bound number of codewords, `n` is the length of the codewords, and `k` is the maximum number of errors it is capable of correcting. Any code that achieves the upper bound of the equation is known as a Perfect Code. As a side note, Richard Hamming developed a perfect code known now as Hamming Codes.



This concludes the second installment of this three-part series on coding theory. Richard Hamming created error correcting codes that addressed the problem of brittle computations in the 1950s. However, they still permeate modern computer science. The concept of Hamming Distance incepted Nearest Neighbor error correction. The quality of error correction is dependent on the Hamming Bound, which is an equation that expresses the three competing goals of an effective code.

Make sure to check back for the final installment of this series. To date, the posts have covered mostly supporting concepts. However, the concluding piece agglomerates all ideas into a cohesive whole with an example. As always, thank you for reading and feel free to contact me with questions or comments.