The Complete Guide To Invariant Theory

What comes to your mind when someone mentions invariant theory? Is this concept as complex as it sounds? What does the concept imply, and is it in any way applicable to real-life scenarios? The following analysis answers all these questions; therefore, read on to understand everything you need to know about invariant theory.

What is Invariant Theory?

The invariant theory is a vital component of mathematics. George Boole started this concept, and over time it has branched to form several other individual disciplines. During its origin, the mathematicians spoke one language of invariants.

Such early England mathematicians as MacMahon, Salmon, Sylvester, Cayley, Alfred Young, Turnbull, Littlewood, and Aitken felt united by the ideology of invariants. Algebraic geometry, algebraic combinatorics, and differential algebra are the living offsprings of the invariant theory. Interestingly, to date, the invariant theory is the only exceptional mathematical theory besides the theory of functions that continues to have a profound and lasting impact on mathematics and its development.

While invariants sounded more logical to a mathematician in the past centuries, they have different meanings and applications today. For instance, the classical invariant theory, a branch of the concept, is once again alive after experts in the near past deemed it as dead and forgotten. Many statisticians and researchers reinvent the classical invariant theory and present it in a more rigorous way that brings back to life the past glory of the concept.

Among them is David Mumford with his current geometry invariants, and Aslaxen, the proponent of the invariant theory of matrices.

As a result, many mathematics lovers are studying the classical invariant theory as an independent mathematical theorem. Let’s go deeper into the history of the invariant theory concept; the concept has had two significant turning points. The first turning point involved the popularization of the concept and creating the current lasting effect.

The second turning point caused a misunderstanding of the concept, and like the latter, this misunderstanding still exists to date. Some scholars define invariant theory as the study of a group’s actions and orbits. At the same time, this definition has some truth; it’s incomplete without a programmatic statement to illustrate the term.

Let’s illustrate the misinterpretations surrounding the invariant theory by looking at some scholarly arguments.

Hermann Weyl, as he introduces his book titled Classical Groups, argues that

  • The disappearance of invariants expresses geometric realities, and,
  • All invariants qualify as invariants of tensors

These statements are pretty confusing. Hermann argues that the disappearance of invariants expresses all geometric realities, yet geometric facts are spaces independent of the choice of coordinate systems. Therefore, you can describe geometric realities using equations with which you must choose particular coordinates.

This negates Hermann’s definition. For instance, space V in a vector with dimension n makes one choose appropriate coordinate systems such as XI, X2, … , Xn. After all, the best way to express geometric realities is to use the above coordinates.

Shockingly, some physicists and mathematicians in the recent past discovered that the regular type of equations that form a commutative ring and are generated by variables XI, X2, … , Xn are insufficient. As such, they cannot describe most physics and geometrical realities.

Boole’s discovery prompted these experts to introduce another ring which they termed the noncommutative polynomial function ring. Like the old ring, this ring applies to coordinates in XI, X2, … , Xn. Interestingly, according to these scholars, this new ring has homogeneous elements, referred to as the homogeneous noncommutative polynomial function. Te elements occur on variables XI, X2, … , Xn, and are better-termed tensors.

Henceforth, if we go by Hermann Weyl’s philosophy, we would accept that the tensor algebra equations are sufficient to describe all geometric realities. Similarly, if these equations express geometric properties, then they shouldn’t influence which coordinates you choose. This implies that an equation must be invariant based on the coordinate changes to describe an inevitable geometric fact.

At length, the concept invariant theory from the days of the inventor Boole refers to translating geometric realities into tensors, a form of algebraic equations. Mumford also includes these elements in his expansion of the concept. Therefore, to translate geometry into algebra, you must decompose the tensor algebra to get several fundamental components based on the change of coordinates.

Alternatively, you can devise a systematic notation to express invariants for every irreducible constituent. The debate around decomposition is a significant advancement to mathematicians worldwide. The decomposition can be illustrated as follows;

Considering the concomitance of variables f(XI, X2, X3), we must have two predominant classes of concomitants for the three variables. This leaves us with symmetric functions to satisfy equations fs(XI,X2,X3) = fs(Xil,Xi2,Xi3). Going forward, we will see that each permutation yielding indices (l, 2, 3) to (i I, i2, i3), and all the skew-symmetric concomitants, described by equations fa (XI, X2, X3) = ±fa (Xii’ Xi2′ Xi3)’ and where the sign is + 1 or -1 based on indices (1, 2, 3) producing permutation to (iI, i2, i3) is odd or even.

Consequently, it’s untrue for a concomitant with three variables, to sum up into skew-symmetric and symmetric functions. Therefore, there has to be a third concomitant (a cyclic function) which we can define using the equation fc(XI, X2, X3) + fc(X3, XI, X2) + fc(X2, X3, xI> =.

Boolean Logic bust of George Boole
A bust of George Boole at the Cork College University. Boole, in his Expose of Linear Transformation, introduced a new mathematics offshoot, the current invariant theory. The invariant theory would later inspire Einstein’s research on the relativity theory.

Invariant Theory: An exact definition

The invariant theory is an offshoot of abstract algebra. It deals with algebraic varieties and group actions. Similarly, vector spaces are a perfect example of an algebraic variety while classifying them based on their function impact.

The invariant theory also refers to the explicit polynomial function’s description, which doesn’t change or is invariant even when transformed from a particular linear group. For instance, if we assume that specific action in a particular linear group is SLn on n’s position by matrix n and by left multiplication, this determinant is invariant. This is because A X determinant is equal to X determinant when A remains in SLn.

The invariant theory has also inspired the development of the invariant theory of matrices. According to the proponents of this theory, polynomial functions result from different sets of matrices invariant upon simultaneous conjugations. However, these polynomial functions must have coefficients at a specific infinite field.

Alternatively, these matrices can have a ring of integers.

How Does Invariant Theory Work?

The invariant theory is critical in artificial intelligence and machine learning. The concept helps machine learning experts understand and apply concepts differently. For instance, in particle physics, invariant theory helps these experts understand that all processes are Lorentz-invariant and can be permutation-invariant considering the particles can be identical. This identity enables these experts to distinguish the particles.

Similarly, machine learning experts use invariant theory to understand the identity of atoms in molecular properties, lattices, translations invariants, rotation invariants, and permutation invariants. Below are the applications of invariant theory in machine learning. Other applications of invariant theory involve computations besides the ones explained below.

Model Construction

Using machine learning algorithms, machine learning experts construct a model by thoroughly training it. The training involves testing the machine for suitability to the data. Similarly, training helps these experts calibrate and fit the machine into the regression inference.

A simple way to train these machines is by converting raw inputs into an invariant in line with the experts’ invariances. After this step, the experts train the machines using these impacts as the reference points. With time, the experts maximally embed the invariance into the particular model in question.

Another method machine learning experts use to train models: transforming raw inputs into a known invariance and training the model based on the resulting transformed data. For instance, in a rotational invariance, experts rotate the original data a specific number of times. After the rotation, the experts use a compilation of resulting rotation data to learn the rotational invariance.

Machine Learning Algorithms

Machine learning experts use two distinguished algorithms; the Random Forest (RF) regressor, which they implement using the sci-kit learn. RFs contain an orchestra of binary decision trees. As such, the experts train each decision tree using a random compilation of data.

The trainers obtain this random data by conducting a random sampling procedure that involves replacements such as bagging. Afterward, these experts equip each offset of the decision tree using an if-then analogy. This logic determines the threshold value and input stage where the experts should split the data to adhere to the Gini impurity score depreciation.

From this procedure, the experts understand that all the data points which end at a similar leaf node have the same output values. Furthermore, these experts incorporate extra randomness into the tree generation procedure using a random subset of input features. This helps them identify the correct split criteria for every branch point.

To obtain a final prediction of the RF regressor, these experts calculate the average prediction of all the particular tress in an orchestra. The machine learning experts prefer RFs because of their ease of execution and robust behavior. RFs only yield the maximum tree depth and ensemble size parameters which are adjustable.

The specialists use the maximum tree depth to understand the trade-off between memory usage and model performance. For example, the total depth trees have maximum performance, although they may need extra memory. However, the RFs with a maximum tree depth scale linearly based on the training data size.

This depends on whether every data point adds some new features to the data set. Simultaneously, the tree depths adjust in a rough and logarithmic sequence depending on the number of tree leaves. These facts help these experts set a maximum tree size for each data set to enhance the efficiency of the model in question.

How Do You Create Invariant Theory?

You can create an invariant theory by assuming you have a display style of G in which G stands for a group and a V display style where V is a specific vector space that has an infinite dimension. Additionally, you should have a K display field in which K represents complex numbers.

The G representation in the V display will be a group homomorphism {\displaystyle \pi :G\to GL(V)}\pi :G\to GL(V), which iinstigates a group action of {\displaystyle G}G on {\displaystyle V}V. Therefore, if {\displaystyle k[V]}k[V] is the space vector for polynomial functions on {\displaystyle V}V, then {\displaystyle G}G’s group action on {\displaystyle V}V yields a {\displaystyle k[V]}k[V] action.

The Formula Would Be:

{\displaystyle (g\cdot f)(x):=f(g^{-1}(x))\qquad \forall x\in V,g\in G,f\in k[V].}{\displaystyle (g\cdot f)(x):=f(g^{-1}(x))\qquad \forall x\in V,g\in G,f\in k[V].}

Fortunately, while using this action you can naturally consider all the polynomial function subspaces. Under this particular group action, these functions are invariant. Your end invariant theory will be {\displaystyle g\cdot f=f}{\displaystyle g\cdot f=f} for all {\displaystyle g\in G}g\in G which you can denote as {\displaystyle k[V]^{G}}k[V]^{G}.

Where Did Invariant Theory Originate From?

The invariant theory has a long and interesting theory. However, its history started in the nineteenth century by the individual we shall discuss below:

George Boole

George Boole, the mathematician who invented invariant theory, was born in Lincolnshire, England, on November 2nd, 1815. He later died on December 8th, 1864, in Ballintemple, Ireland. He’s famous for establishing the modern symbol logic the Boolean algebra used in designing advanced computer circuits.

Boole acquired his mathematics skills from his father, who also taught him how to design opticals. He grew his mathematical skills by self-training. By sixteen years, he was teaching in several village schools, after which he opened his school at twenty. Boole spent all his leisure time reading mathematics.

Significantly, in 1841 Boole submitted an expert research paper to mathematical experts. His research was about analytical transformations and differential equations, and the linear transformation algebraic hurdle. All of Boole’s papers emphasized invariance and later wrote a paper on how you can combine calculus and algebra.

In the same year, 1841, Boole, in his Expose of Linear Transformation, introduced a new mathematics offshoot, the current invariant theory. The invariant theory would later inspire Einstein’s research on the relativity theory.

Invariant Theory - machine learning
Machine learning and artificial intelligence illustrated. The computer displays an illustrative screen with an automated wireless Robot arm.

What Are the Applications of Invariant Theory?

Invariant theory, complex as it may sound to many people, has many practical applications to our day-to-day life. Below are some of the applications of invariant theory:

Markov Chain

The Markov chain is the random transition process from a particular state-space to another. It is a memory-less process, although the stages are interrelated. This means that you can only predict the subsequent state distribution based on the current state distribution. Interestingly, this next stage has nothing completely to do with the original state before the transition.

The Markov chain uses invariant theory to determine the probability of random processes which two interrelated mechanisms generate. One state has a defined number of states in the Markov chain, while the other has several random functions. Markov chain helps mathematicians to identify random transitions between a system’s states.

Mathematicians use the Markov chain to create model sequences in applied algorithms. Markov chain also helps experts make predictions in robotic surveillance, transformer health estimations, parallel computations, data modeling, and information freshness.

Blockchain Mining

Other applications of Invariant theory involve blockchain mining technologies. Since blockchain mining’s queuing system uses nodes, it borrows a lot from invariant theory. Experts also use invariant theory to determine method improvement and performance analysis strategies for blockchain queuing systems.

Using invariant theory, these experts define vital relations among basic parameters to process bitcoin confirmations successfully. The bitcoin confirmation timelines follow a specific function on a probability distribution.

Examples of invariant theory In the Real World

Ordinary life situations use invariant theory. Examples include;


The current geometric invariant theory by David Mumford is one of the applications of invariant theory. According to Mumford, a group action must capture an invariant’s details using the coordinate ring for a quotient to exist. Mumford’s concept is extensively in use in different embedding technologies.


Anytime you are counting something, you apply the invariant theory. For instance, regardless of what number you’ll start counting from, the numbers will never change. This implies that numbers are invariants.

Computer Science

Another common application of the invariant theory is computer programs executions. Computer scientists use invariant theory to create a logical assertion to determine whether or not a computer application is correct.

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