행렬 지수: 미분 방정식 풀기 및 흐름 시각화하기

행렬 지수는 이상하게 보이지만 미분 방정식을 풀고 양자역학과 관계에서 중요한 역할을 합니다.

### 미분 방정식의 행렬 지수화 이해하기
* 🔢 행렬 지수는 특히 물리학 및 공학에서 **특정 유형의 미분 방정식을 푸는 데 사용되는 강력한 연산**입니다.
* 이 연산은 **지수 함수에 대한 테일러 급수**를 사용하여 정의되며, 지수의 개념을 행렬로 확장합니다.
* 🔄 정사각형 행렬의 경우 행렬 지수화의 결과는 **각각 계승에 의해 스케일링된 무한한 행렬 거듭제곱을 합산**하여 계산됩니다.
* 🔍 행렬 지수는 벡터의 변화율이 **행렬에 벡터 자체를 곱한 값**으로 결정되는 미분 방정식 시스템을 푸는 데 특히 유용합니다.
* 🔄 이러한 시스템에 대한 해법은 **초기 상태 벡터에 계수 행렬의 지수 곱하기 시간**을 곱하는 경우가 많습니다.
* 🔄 기하학적 용어로 **행렬 지수는 계수 행렬의 속성을 기반**으로 벡터 공간에서 회전 또는 변환을 생성하는 것으로 이해할 수 있습니다.
* 🔀 90도 회전 행렬의 예는 **행렬 지수가 복잡한 행렬에서도 예측 가능한 결과**를 생성할 수 있는 방법을 설명합니다.
* 행렬 지수는 시간에 따른 양자 상태의 진화를 설명하는 **슈뢰딩거 방정식에서 핵심적인 역할**을 하는 양자 역학의 핵심 개념입니다.

Let me pull out an old differential equations textbook that I learned from in college, and let's turn to this funny little exercise in here that asks the reader to compute E to the power a t, where a we're told is going to be a matrix, and the insinuation seems to be that the results will also be a matrix. It then offers several examples for what you might plug in for a. Now, taking out a context, putting a matrix into an exponent like this probably seems like total nonsense, but what it refers to is an extremely beautiful operation, and the reason it shows up in this book is that it's useful. It's used to solve a very important class of differential equations. In turn, given that the universe is often written in the language of differential equations, you see this pop up in physics all the time too, especially in quantum mechanics, where matrix exponents are littered throughout the place. They play a particularly prominent role.

This has a lot to do with Schrodinger's equation, which we'll touch on a bit later, and it may also help in understanding your romantic relationships, but again, all in due time. A big part of the reason I want to cover this topic is that there is an extremely nice way to visualize what matrix exponents are actually doing using flow that not a lot of people seem to talk about, but for the bulk of this chapter, let's start by laying out what exactly the operation is, and see if we can get a feel for what kinds of problems it helps us to solve. The first thing you should know is that this is not some bizarre way to multiply the constant e by itself multiple times. You would be right to call that nonsense. The actual definition is related to a certain infinite polynomial for describing real number powers of e, what we call its Taylor series. For example, if I took the number 2 and plugged it into this polynomial,

then as you add more and more terms, each of which looks like some power of 2 divided by some factorial. The sum approaches a number near 7.389, and this number is precisely e times e. If you increment this input by one, then somewhat miraculously, no matter where you started from, the effect on the output is always to multiply it by another factor of e. For reasons that you're going to see in a bit, mathematicians became interested in plugging all kinds of things into this polynomial, things like complex numbers, and for our purposes today, matrices, even when those objects do not immediately make sense as exponents. What some authors do is give this infinite polynomial the name exp when you plug in more exotic inputs. It's a gentle nod to the connection that this has to exponential functions in the case of real numbers, even though obviously these inputs don't make sense as exponents. However, an equally common convention is to give a much less gentle nod to the connection

and just abbreviate the whole thing as e to the power of whatever object you're plugging in, whether that's a complex number or a matrix or all sorts of more exotic objects. So while this equation is a theorem for real numbers, it's a definition for more exotic inputs. Cynically, you could call this a blatant abuse of notation. More charitably, you might view it as an example of the beautiful cycle between discovery and invention in math. In either case, plugging in a matrix even to a polynomial might seem a little strange, so let's be clear on what we mean here. The matrix has to have the same number of rows and columns. That way you can multiply it by itself according to the usual rules of matrix multiplication. This is what we mean by squaring it. Similarly, if you were to take that result and then multiply it by the original matrix again, this is what we mean by cubing the matrix.

If you carry on like this, you can take any whole number power of a matrix. It's perfectly sensible. In this context, powers still mean exactly what you would expect: repeated multiplication. Each term in this polynomial is scaled by one divided by some factorial, and with matrices, all that means is that you multiply each component by that number. Likewise, it always makes sense to add together two matrices. This is something you again do term by term. The astute among you might ask how sensible it is to take this out to infinity, which would be a great question, one that I'm largely going to postpone the answer to, but I can show you one pretty fun example here now. Take this 2x2 matrix that has negative pi and pi sitting off its diagonal entries. Let's see what the sum gives. The first term is the identity matrix. This is actually what we mean by definition when we raise a matrix to the zeroth power.

Then we add the matrix itself, which gives us the pi off the diagonal terms, and then add half of the matrix squared, and continuing on. I'll have the computer keep adding more and more terms, each of which requires taking one more matrix product to get the new power, and then adding it to a running tally. And as it keeps going, it seems to be approaching a stable value, which is around negative one times the identity matrix. In this sense, we say the infinite sum equals that negative identity. By the end of this video, my hope is that this particular fact comes to make total sense to you. For any of you familiar with Euler's famous identity, this is essentially the matrix version of that. It turns out that in general, no matter what matrix you start with, as you add more and more terms, you eventually approach some stable value, though sometimes it can take quite a while before you get there. Just seeing the definition like this in isolation raises all kinds of questions.

most notably, why would mathematicians and physicists be interested in torturing their poor matrices this way? What problems are they trying to solve? And if you're anything like me, a new operation is only satisfying when you have a clear view of what it's trying to do, some sense of how to predict the output based on the input before you actually crunch the numbers. How on earth could you have predicted that the matrix with pi off the diagonals results in a negative identity matrix like this? Often in math you should view the definition not as a starting point, but as a target. Contrary to the structure of textbooks, mathematicians do not start by making definitions and then listing a lot of theorems and proving them and then showing some examples. The process of discovering math typically goes the other way around. They start by chewing on specific problems, and then generalizing those problems, then coming up with constructs that might be helpful in those general cases.

and only then do you write down a new definition, or extend an old one. As to what sorts of specific examples might motivate matrix exponents, two come to mind. One involving relationships, and the other quantum mechanics. Let's start with relationships. Suppose we have two lovers, let's call them Romeo and Juliet, and let's let x represent Juliet's love for Romeo, and y represent his love for her, both of which are going to be values that change with time. This is an example we actually touched on in chapter 1, based on a Steven Strogatz article, but it's okay if you didn't see that. The way their relationship works is that the rate at which Juliet's love for Romeo changes, the derivative of this value, is equal to negative one times Romeo's love for her. So in other words, when Romeo is expressing cool disinterest, that's when Juliet's feelings actually increase, whereas if he becomes too infatuated, her interest will start to fade.

Romeo, on the other hand, is the opposite. The rate of change of his love is equal to the of Juliet's love, so while Juliet is mad at him, his affections tend to decrease, whereas if she loves him, that's when his feelings grow. Of course, neither one of these numbers is holding still. As Romeo's love increases in response to Juliet, her equation continues to apply and drives her love down. Both of these equations always apply, from each infinitesimal point in time to the next, so every slight change to one value immediately influences the rate of change of the other. This is a system of differential equations. It's a puzzle, where your challenge is to find explicit functions for x of t and y of t that make both of these expressions true. Now, as systems of differential equations go, this one is on the simpler side, enough so that many calculus students could probably just guess at an answer.

But keep in mind, it's not enough to find some pair of functions that makes this true. If you want to actually predict where Romeo and Juliet end up after some starting point, you have to make sure that your functions match the initial set of conditions at time t equals 0. More to the point, our actual goal today is to systematically solve more general versions of this equation, without guessing and checking, and it's that question that leads us to matrix exponents. Very often when you have multiple changing values like this, it's helpful to package them together as coordinates of a single point in a higher dimensional space. So for Romeo and Juliet, think of their relationship as a point in a 2D space, the x-coordinate capturing Juliet's feelings, and the y-coordinate capturing Romeo's. Sometimes it's helpful to picture this as an arrow from the origin, other times just as a point. All that really matters is that it encodes two numbers.

and moving forward, we'll be writing that as a column vector. And of course, this is all a function of time. You might picture the rate of change of this state, the thing that packages together the derivative of x and the derivative of y, as a kind of velocity vector in this state space, something that tugs at our point in some direction and with some magnitude that indicates how quickly it's changing. Remember, the rule here is that the rate of change of x is negative y, and the rate of change of y is x. Set up as vectors like this, we could rewrite the right-hand side of this equation as a product of this matrix with the original vector xy. The top row encodes Juliet's rule, and the bottom row encodes Romeo's rule. So what we have here is a differential equation telling us that the rate of change of some vector is equal to a certain matrix times itself. In a moment, we'll talk about how matrix exponentiation solves this kind of equation.

but before that, let me show you a simpler way that we can solve this particular system, one that uses pure geometry, and it helps set the stage for visualizing matrix exponents a bit later. This matrix from our system is a 90-degree rotation matrix. For any of you rusty on how to think about matrices as transformations, there's a video all about it on this channel, a series really. The basic idea is that when you multiply a matrix by the vector 1 0, it pulls out the first column, and similarly if you multiply it by 0 1, that pulls out the second column. What this means is that when you look at a matrix, you can read its columns as telling you what it does to these two vectors, known as the matrix. The way it acts on any other vector is a result of scaling and adding these two basis results by that vector's coordinates. So looking back at the matrix from our system,

notice how from its columns, we can tell it takes the first basis vector to 0, 1, and the second to negative 1, 0, hence why I'm calling it the 90-degree rotation matrix. What it means for our equation is that it's saying wherever Romeo and Juliet are in this space, their rate of change has to look like a 90-degree rotation of this position vector. The only way velocity can permanently be perpendicular to position like this is when you rotate around the origin in circular motion, never growing or shrinking because the rate of change has no component in the direction of the position. More specifically, since the length of this velocity vector equals the length of the position vector, then for each unit of time, the distance that this covers is equal to one radius's worth of arc length along that circle. In other words, it rotates at one radian per unit time, so in particular, it would take 2 pi units of time to make a full revolution.

If you want to describe this kind of rotation with a formula, we can use a more general rotation matrix, which looks like this. Again, we can read it in terms of the columns. Notice how the first column tells us that it takes that first basis vector to cos t sin t, and the second column tells us that it takes the second basis vector to negative sin t cos t, both of which are consistent with rotating by t radians. So, to solve the system, if you want to predict where Romeo and Juliet end up after t units of time, you can multiply this matrix by their initial state. The active viewers among you might also enjoy taking a moment to pause and confirm that the explicit formulas you get out of this for x of t and y of t really do satisfy the system of differential equations that we started with. The mathematician in you might wonder if it's possible to solve not just this specific system, but equations like it for any other matrix, no matter its coefficients.

To ask this question is to set yourself up to rediscover matrix exponents. The main goal for today is for you to understand how this equation lets you intuitively picture the operation, which we write as e raised to a matrix, and on the flip side, how being able to compute matrix exponents lets you explicitly solve this equation. A much less whimsical example is Schrodinger's famous equation, which is the fundamental equation describing how systems in quantum mechanics change over time. It looks pretty intimidating, and I mean it's quantum mechanics, so of course it will, but it's actually not that different from the Romeo-Juliet setup. This symbol here refers to a certain vector. It's a vector that packages together all the information you might care about in a system, like the various particles' positions and momenta. It's analogous to our simpler 2D vector that encoded all the information about Romeo and Juliet. The equation says that the rate at which this state

Vector changes look like a certain matrix times itself. There are a number of things that make Schrodinger's equation notably more complicated, but in the back of your mind, you might think of it as a target point that you and I can build up to, with simpler examples like Romeo and Juliet offering more friendly stepping stones along the way. Actually, the simplest example, which is tied to ordinary real number powers of e, is the one-dimensional case. This is when you have a single changing value, and its rate of change equals some constant times itself. So the bigger the value, the faster it grows. Most people are more comfortable visualizing this with a graph, where the higher the value of the graph, the steeper its slope, resulting in this ever-steepening upward curve. Just keep in mind that when we get to higher-dimensional variance, graphs are a lot less helpful. This is a highly important equation in its own right.

It's a very powerful concept when the rate of change of a value is proportional to the value itself. This is the equation governing things like compound interest, or the early stages of population growth before the effects of limited resources kick in, or the early stages of an epidemic while most of the population is susceptible. Calculus students all learn about how the derivative of e to the rt is r times itself. In other words, this self-reinforcing growth phenomenon is the same thing as exponential growth, and e to the rt solves this equation. Actually, a better way to think about it is that there are many different solutions to this equation, one for each initial condition, something like an initial investment size or an initial population, which we'll just call x0. Notice, by the way, how the higher the value for x0, the higher the initial slope of the resulting solution, which should make complete sense given the equation. The function e to the rt is just a solution when the initial condition is 1.

but if you multiply by any other initial condition, you get a new function which still satisfies this property. It still has a derivative which is r times itself, but this time it starts at x0 since e to the 0 is 1. This is worth highlighting before we generalize to more dimensions. Do not think of the exponential part as being a solution in and of itself. Think of it as something that acts on an initial condition in order to give a solution. You see, up in the two-dimensional case, when we have a changing vector whose rate of change is constrained to be some matrix times itself, what the solution looks like is also an exponential term acting on a given initial condition, but the exponential part in that case will produce a matrix that changes with time, and the initial condition is a vector. In fact, you should think of the definition of matrix exponentiation as being heavily motivated by making sure that this fact is true. For example, if we look back at the system that popped up with Romeo and Juliet,

the claim now is that solutions look like e raised to this 0, negative 1, 1, 0 matrix all times time multiplied by some initial condition. But we've already seen the solution in this case, we know it looks like a rotation matrix times the initial condition. So let's take a moment to roll up our sleeves and compute the exponential term using the definition that I mentioned at the start, and see if it lines up. Remember, writing e to the power of a matrix is a shorthand, a shorthand for plugging it in to this long infinite polynomial, the Taylor series for e to the x. I know it might seem pretty complicated to do this, but trust me, it's very satisfying how this particular one turns out. If you actually sit down and you compute successive powers of this matrix, what you'd notice is that they fall into a cycling pattern every four iterations. This should make sense given that we know it's a 90 degree rotation matrix.

So when you add together all infinitely many matrices term by term, each term in the result looks like a polynomial in t with some nice cycling pattern in its coefficients, all of them scaled by the relevant factorial term. Those of you who are savvy with Taylor series might be able to recognize that each one of these components is the Taylor series for either sine or cosine, though in that top right corner's case it's actually negative sine. So what we get from the computation is exactly the rotation matrix we had from before. To me, this is extremely beautiful. We have two completely different ways of reasoning about the same system, and they give us the same answer. It's reassuring that they do, but it's wild just how different the mode of thought is when you're chugging through this polynomial versus when you're geometrically reasoning about what a velocity perpendicular to a position must imply. Hopefully the fact that these line up inspires a little confidence in the claim that matrix exponents really do solve systems like this.

This explains the computation we saw at the start, by the way, with the matrix that had negative pi and pi off the diagonals, producing the negative identity. This expression is exponentiating a 90 degree rotation matrix times pi, which is another way to describe what the Romeo-Juliet setup does after pi units of time. As we now know, that has the effect of rotating everything 180 degrees in this state space, which is the same as multiplying by negative 1. Also, for any of you familiar with imaginary number exponents, this particular example is ringing a ton of bells. It is 100% analogous. In fact, we could have framed the entire example where Romeo and Juliet's feelings were packaged into a complex number, and the rate of change of that complex number would have been i times itself, since multiplication by i also acts like a 90 degree rotation. The same exact line of reasoning, both analytic and geometric.

would have led to this whole idea that e to the power i t describes rotation. These are actually two of many different examples throughout math and physics when you find yourself exponentiating some object, which acts as a 90-degree rotation times time. It shows up with quaternions or many of the matrices that pop up in quantum mechanics. In all of these cases, we have this really neat general idea that if you take some operation that rotates 90 degrees in some plane, often it's a plane in some high-dimensional space that we can't visualize, then what we get by exponentiating that operation times time is something that generates all other rotations in that same plane. One of the more complicated variations on this same theme is Schrodinger's equation. It's not just that this has the derivative of a state equals some matrix times that state form. The nature of the relevant matrix here is such that the equation also describes a kind of rotation, though in many applications of

Schrodinger's equation, it'll be a rotation in a kind of function space. It's a little more involved, though, because typically, there's a combination of many different rotations. It takes time to really dig into this equation, and I would love to do that in a later chapter, but right now I cannot help but at least allude to the fact that this imaginary unit i that sits so impishly in such a fundamental equation for all of the universe is playing basically the same role as the matrix from our Romeo-Julia example. What this i communicates is that the rate of change of a certain state is, in a sense, perpendicular to that state, and hence that the way things have to evolve over time will involve a kind of oscillation. But matrix exponentiation can do so much more than just rotation. You can always visualize these sorts of differential equations using a vector field. The idea is that this equation tells us the velocity of a state is entirely determined

by its position. So what we do is go to every point in the space and draw a little vector indicating what the velocity of a state must be if it passes through that point. For our type of equation, this means that we go to each point v in space and we attach the vector m times v. To intuitively understand how any given initial condition will evolve, you let it flow along this field with a velocity always matching whatever vector it's sitting on at any given point in time. So if the claim is that solutions to this equation look like e to the m t times some initial condition, it means you can visualize what the matrix e to the m t does by letting every possible initial condition flow along this field for t units of time. The transition from start to finish is described by whatever matrix pops out from the computation for e to the m t. In our main example with the 90-degree rotation matrix, the vector field looks like this, and as we saw e to the m t

describes rotation in that case, which lines up with flow along this field. As another example, the more Shakespearean Romeo and Juliet might have equations that look a little more like this, where Juliet's rule is symmetric with Romeo's, and both of them are inclined to get carried away in response to one another's feelings. Again, the way the vector field you're looking at has been defined is to go to each point v in space and attach the vector m times v. This is the pictorial way of saying that the rate of change of a state must always equal m times itself. But for this example, flow along the field looks a lot different from how it did before. If Romeo and Juliet start off anywhere in this upper right half of the plane, their feelings will feed off of each other and they both tend towards infinity. If they're in the other half of the plane, well let's just say that they stay more true to their in Montague family traditions.

So even before you try calculating the exponential of this particular matrix, you can already have an intuitive sense for what the answer should look like. The resulting matrix should describe the transition from time 0 to time t, which if you look at the field seems to indicate that it will squish along one diagonal while stretching along another, getting more extreme as t gets larger. Of course, all of this is presuming that e to the m t times an initial condition actually solves these systems. This is one of those facts that's easiest to believe when you just work it out yourself. But I'll run through a quick rough sketch. Write out the full polynomial that defines e to the m t and multiply by some initial condition vector on the right. And then take the derivative of this with respect to t. Because the matrix m is constant, this just means applying the power rule to each one of the terms. And that power rule really nicely cancels out with the factorial terms.

So what we're left with is an expression that looks almost identical to what we had before, except that each term has an extra m hanging on to it. But this can be factored out to the left. So the derivative of the expression is m times the original expression, and hence it solves the equation. This actually sweeps under the rug some details required for rigor, mostly centered around the question of whether or not this thing actually converges, but it does give the main idea. In the next chapter, I would like to talk more about the properties that this operation has, most notably its relationship with eigenvectors and eigenvalues, which leads us to more concrete ways of thinking about how you actually carry out this computation, which otherwise seems insane. Also, time permitting, it might be fun to talk about what it means to raise e to the power of the derivative operator. Thank you.

How (and why) to raise e to the power of a matrix | DE6

My name is Grant Sanderson. Videos here cover a variety of topics in math, or adjacent fields like physics and CS, all with an emphasis on visualizing the core ideas. The goal is to use animation to help elucidate and motivate otherwise tricky topics, and for difficult problems to be made simple with changes in perspective.

For more information, other projects, FAQs, and inquiries see the website: https://www.3blue1brown.com

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행렬 지수는 단순한 수학적 기이함이 아니라 미분 방정식을 푸는 데 유용한 연산입니다.

행렬지수는 물리학, 특히 양자역학과 슈뢰딩거 방정식에서 두드러지게 사용됩니다.

행렬 지수는 흐름을 사용하여 독특한 방식으로 시각화할 수 있으며, 행렬의 제곱과 세제곱이라는 개념으로 행렬을 곱하는 데 중요한 역할을 합니다.

행렬의 거듭제곱은 반복적인 곱셈을 포함하며 무한대까지 취해 안정된 값에 가까워질 수 있습니다.

특정 행렬의 무한 거듭제곱의 합은 안정적인 음의 행렬이 될 수 있습니다.

수학자와 물리학자들은 특히 행렬 연산이 시간에 따른 값 변화의 역학을 모델링하는 데 도움이 되는 관계 및 양자역학의 맥락에서 다양한 문제를 해결하기 위해 행렬 지수에 관심을 갖고 있습니다.

로미오와 줄리엣의 사랑의 미분 방정식 이해하기

로미오와 줄리엣의 사랑은 상대방의 애정에 따라 변화하며 미분 방정식의 체계를 만들어냅니다.

두 사람의 변화하는 감정 사이의 관계는 2D 공간의 한 점으로 표현할 수 있으며, 사랑의 변화율은 속도 벡터를 형성합니다.

이 방정식 시스템은 행렬 지수화 또는 기하학적 변환을 사용하여 풀 수도 있습니다.

90도 회전 매트릭스와 그 중요성 이해하기

90도 회전 행렬은 위치에 수직인 속도를 나타내며 원점을 중심으로 한 원운동을 나타냅니다.

행렬은 주어진 원 운동에서 일정 시간이 지난 후 물체의 위치를 예측하는 데 사용할 수 있습니다.

행렬 지수의 개념을 사용하면 방정식의 명시적인 해를 구하고 연산을 직관적으로 시각화할 수 있습니다.

기하급수적 성장 현상에 대한 이해

기하급수적 성장의 개념은 그래프에서 변화율이 현재 값에 비례하는 자기 강화 상승 곡선으로 시각화할 수 있습니다.

이 현상을 설명하는 방정식은 복리 이자 및 인구 증가와 같은 다양한 분야에서 매우 중요합니다.

함수 e에서 rt는 초기 조건 1에 대한 방정식의 해를 나타내지만, 모든 초기 조건인 x0에 대해 일반화할 수 있어 지수 성장의 자기 복제 특성을 강조합니다.

변화하는 벡터의 변화율이 행렬에 의해 결정되는 고차원의 경우, 이 해법은 초기 조건 벡터에 작용하는 행렬 지수화를 포함하며 기하급수적 성장 개념의 폭넓은 적용을 보여줍니다.

행렬의 지수는 테일러 급수에 e를 x의 거듭제곱으로 연결한 약식으로 표현할 수 있으며, 90도 회전 행렬의 경우 4회 반복마다 순환 패턴이 생성됩니다.

행렬의 연속 거듭제곱을 계산하면 결과가 사인과 코사인에 대한 테일러 급수와 동일하여 궁극적으로 기하학적으로 동일한 회전 행렬을 생성한다는 것을 알 수 있습니다.

이는 행렬 지수를 사용하여 동일한 시스템을 풀 때 서로 다른 두 가지 추론 방식(다항식 계산과 기하학적 해석) 간의 아름다운 조화를 보여주며, 이러한 시스템을 푸는 능력에 대한 자신감을 제공합니다.

수학 및 물리학에서 회전과 진화를 설명하는 지수 함수의 역할

특정 연산을 지수화하면 고차원 공간에서 90도 회전을 나타내며, 시간이 지남에 따라 같은 평면에서 다른 모든 회전을 생성합니다.

슈뢰딩거 방정식도 함수 공간에서 일종의 회전을 포함하며, 가상의 단위인 'i'는 수직 변화율과 진동을 나타내는 데 중요한 역할을 합니다.

행렬 지수를 사용하면 초기 조건이 시간에 따라 어떻게 변화하는지를 시각화할 수 있으며, 계산된 e 행렬을 통해 시작에서 끝까지의 전환을 설명할 수 있습니다.

행렬 지수화와 벡터 필드와의 관계에 대한 이해

행렬 지수는 상태의 변화율을 그림으로 설명하는 방법으로, 결과 행렬은 시간에 따른 변화를 보여줍니다.

e를 미분 연산자의 거듭제곱으로 올리는 과정은 고유 벡터 및 고유값과 밀접한 관련이 있으며, 이 계산을 이해하는 구체적인 방법을 제공합니다.

벡터장을 따라 흐르는 연산과 연결하면 시간에 따라 시스템에 어떤 영향을 미치는지 직관적으로 파악할 수 있으며, 수렴에 대한 자세한 내용은 간결하게 설명하기 위해 양탄자 아래에 숨겨져 있습니다.