Eigenvectors and eigenvalues | Chapter 14, Essence of linear algebra

3Blue1Brown
15 Sept 201617:15
EducationalLearning
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TLDRThis video addresses the common confusion surrounding eigenvectors and eigenvalues in linear algebra, emphasizing the importance of visual understanding and foundational concepts like linear transformations, determinants, and change of basis. It explains how certain vectors, called eigenvectors, remain on their span during transformations, only being scaled by a factor known as the eigenvalue. The video also covers how to find eigenvectors and eigenvalues, the significance of diagonal matrices, and the concept of an eigenbasis, illustrating their practical applications in simplifying complex matrix operations.

Takeaways
  • 馃 Eigenvectors and eigenvalues are often misunderstood due to a lack of visual understanding and a shaky foundation in prerequisite topics like linear transformations, determinants, and change of basis.
  • 馃摎 The concept of eigenvectors is straightforward but requires a solid visual understanding of matrices as linear transformations.
  • 馃攳 Eigenvectors are special vectors that remain on their own span after a linear transformation, being stretched or compressed by a scalar factor, known as the eigenvalue.
  • 馃搹 An eigenvector's eigenvalue indicates the factor by which the vector is stretched or compressed during a transformation, and can be positive, negative, or even imaginary.
  • 馃寪 In a 3D rotation, finding an eigenvector that remains on its span reveals the axis of rotation, simplifying the understanding of the transformation.
  • 馃敘 The computational process for finding eigenvectors and eigenvalues involves setting up the equation (A - 位I)v = 0, where A is the matrix, 位 is the eigenvalue, I is the identity matrix, and v is the eigenvector.
  • 馃搲 To find eigenvalues, one must solve for when the determinant of (A - 位I) is zero, which can result in a polynomial equation in 位.
  • 馃摎 Understanding eigenvectors and eigenvalues can simplify complex transformations, such as diagonalizing matrices to make operations like raising a matrix to a power much simpler.
  • 馃攧 A matrix with an eigenbasis, where the basis vectors are eigenvectors, results in a diagonal matrix with eigenvalues on the diagonal, facilitating easier computations.
  • 馃寑 Some transformations, like a 90-degree rotation, do not have eigenvectors because they rotate every vector off its span, indicated by the absence of real eigenvalues.
  • 馃搱 Shear transformations, however, have eigenvectors and can be represented by a matrix with a single eigenvalue, showing that all vectors in a certain direction remain fixed.
Q & A
  • What is the main challenge students face when learning about eigenvectors and eigenvalues?

    -The main challenge students face is the lack of intuitive understanding, often due to a shaky foundation in prerequisite topics such as linear transformations, determinants, linear systems of equations, and change of basis.

  • Why are eigenvectors and eigenvalues important in the context of linear transformations?

    -Eigenvectors and eigenvalues are important because they provide a way to understand the effect of a linear transformation by identifying vectors that remain on their span and the factor by which they are stretched or compressed.

  • What does it mean for a vector to remain on its span during a transformation?

    -A vector remains on its span during a transformation if the transformation only scales the vector (stretches or compresses it) without changing its direction, effectively not rotating it off its original line.

  • What is the geometric interpretation of eigenvalues?

    -Eigenvalues represent the factor by which an eigenvector is stretched or compressed during a transformation, and they can be positive, negative, or even imaginary numbers.

  • Can you provide an example of a transformation that does not have eigenvectors?

    -A 90-degree rotation in two dimensions does not have eigenvectors because it rotates every vector off its own span, and its matrix has determinant zero, indicating no real eigenvalues.

  • What is the significance of finding an eigenvector for a 3D rotation?

    -Finding an eigenvector for a 3D rotation allows you to identify the axis of rotation, simplifying the understanding of the rotation to just an axis and an angle, rather than dealing with the full 3x3 matrix.

  • How does the process of finding eigenvectors and eigenvalues relate to the concept of determinants?

    -To find eigenvectors and eigenvalues, one must solve for the values of lambda that make the determinant of (A - 位I) zero, where A is the matrix representing the transformation, I is the identity matrix, and 位 is the eigenvalue.

  • What is an eigenbasis and why is it useful?

    -An eigenbasis is a set of basis vectors that are also eigenvectors of a transformation. It is useful because it allows the transformation matrix to be represented as a diagonal matrix with eigenvalues on the diagonal, simplifying computations such as raising the matrix to a power.

  • How can changing the coordinate system to an eigenbasis simplify matrix operations?

    -Changing to an eigenbasis simplifies matrix operations because the transformation matrix in this new system is diagonal, making it easier to perform operations like matrix multiplication and exponentiation.

  • What is the computational process for finding eigenvectors once eigenvalues are known?

    -Once eigenvalues are known, you plug the eigenvalue into the matrix (A - 位I) and solve the resulting system of linear equations to find the eigenvectors that correspond to that eigenvalue.

  • Can you explain the concept of a shear transformation in relation to eigenvectors?

    -A shear transformation moves every vector in a certain direction while keeping one basis vector fixed. In such a transformation, all vectors along the fixed basis vector remain unchanged and are eigenvectors with eigenvalue 1, but there are no other eigenvectors to span the full space.

Outlines
00:00
馃 Understanding Eigenvectors and Eigenvalues

This paragraph introduces the concept of eigenvectors and eigenvalues, which are often challenging for students to grasp intuitively. The speaker suggests that confusion often stems from a lack of visual understanding of prerequisite topics such as matrices as linear transformations, determinants, linear systems, and change of basis. The paragraph uses a two-dimensional linear transformation example to illustrate how certain vectors (eigenvectors) remain on their span after the transformation, being merely stretched or compressed by a scalar amount (the eigenvalue). The basis vector i-hat and the vector -1,1 are given as examples of such eigenvectors, with eigenvalues of 3 and 2, respectively. The paragraph emphasizes the importance of a solid foundation in related topics for a proper understanding of eigenconcepts.

05:00
馃攳 The Computational Aspect of Eigenvectors

The second paragraph delves into the computational side of finding eigenvectors and eigenvalues. It explains the symbolic representation of an eigenvector equation, where a matrix A represents a transformation, v is the eigenvector, and lambda is the eigenvalue. The process involves rewriting the equation to find values of v and lambda that satisfy the condition that A*v equals lambda times v. The paragraph describes the method of creating a new matrix (A - lambda*I) and finding non-zero vectors v that, when multiplied by this matrix, result in the zero vector, indicating an eigenvector. It also touches on the importance of determinants in this process, explaining that an eigenvalue is found when the determinant of the matrix (A - lambda*I) is zero. The speaker uses a concrete example with a matrix and shows how tweaking lambda can lead to finding eigenvalues and subsequently the eigenvectors.

10:03
馃摎 Examples and Properties of Eigenvectors

This paragraph provides examples and properties of eigenvectors, including the case of a 2D transformation that lacks eigenvectors, such as a 90-degree rotation. It discusses how the absence of real eigenvalues in such cases indicates the lack of eigenvectors. The paragraph also explores the concept of a shear, which has eigenvectors with an eigenvalue of 1, and the idea that a matrix can have a single eigenvalue with multiple eigenvectors, as in the case of a matrix that scales everything by a factor of 2. The discussion highlights the importance of understanding the geometric implications of eigenvectors and eigenvalues and their role in simplifying the representation of transformations.

15:04
馃寪 The Power of an Eigenbasis

The final paragraph introduces the concept of an eigenbasis, where the basis vectors of a space are also eigenvectors of a transformation. It explains how using an eigenbasis simplifies matrix operations, especially when raising a matrix to a high power, as each application of the matrix corresponds to raising the eigenvalues to that power. The paragraph also discusses the process of changing to an eigenbasis by using a change of basis matrix and its inverse to transform the original transformation matrix into a diagonal matrix with eigenvalues on the diagonal. It emphasizes the utility of an eigenbasis in making complex transformations more manageable and concludes with an invitation for the audience to explore this concept further through a puzzle and a teaser for the next video on abstract vector spaces.

Mindmap
Keywords
馃挕Eigenvectors
Eigenvectors are special vectors that, when a linear transformation is applied, only change in scale but do not alter their direction. In the context of the video, eigenvectors are highlighted as vectors that remain on their span after a transformation, such as stretching or compression. The script uses the example of a basis vector i-hat being an eigenvector that is stretched by a factor of 3 along the x-axis, illustrating the concept.
馃挕Eigenvalues
Eigenvalues are scalar values that represent the factor by which an eigenvector is stretched or compressed during a linear transformation. They are intrinsically linked to eigenvectors, as each eigenvector has a corresponding eigenvalue. The video script explains that eigenvalues can be positive, negative, or even imaginary, and they indicate the nature of the transformation's effect on the eigenvector.
馃挕Linear Transformations
Linear transformations are functions that map vectors from one space to another while preserving the operations of vector addition and scalar multiplication. The script discusses how linear transformations can be represented by matrices and how they affect vectors, particularly eigenvectors, which remain on their span after the transformation.
馃挕Determinants
Determinants are a value derived from the elements of a square matrix and provide important information about the matrix, such as whether it is invertible. In the script, determinants are used to find eigenvalues by setting the determinant of (A - 位I) to zero, where A is the matrix representing the transformation, 位 is the eigenvalue, and I is the identity matrix.
馃挕Matrix
A matrix is a rectangular array of numbers arranged in rows and columns, often used to represent linear transformations. The video script explains how matrices can be used to represent transformations that affect vectors, and how the columns of a matrix can be interpreted as the images of the basis vectors under that transformation.
馃挕Diagonal Matrix
A diagonal matrix is a special type of matrix where all off-diagonal entries are zero, and only the diagonal entries may be non-zero. The script mentions diagonal matrices in the context of an eigenbasis, where the matrix representing a transformation has eigenvalues on its diagonal, making it easier to perform operations like raising the matrix to a power.
馃挕Change of Basis
Change of basis is a process in linear algebra where one switches from one set of basis vectors to another. The script briefly touches on this concept, explaining how one can represent a transformation in a new coordinate system where the basis vectors are eigenvectors, resulting in a diagonal matrix with eigenvalues on the diagonal.
馃挕Eigenbasis
An eigenbasis is a set of basis vectors for a vector space where each vector is an eigenvector of a given linear transformation. The script explains the concept of an eigenbasis as a useful tool when many eigenvectors are available to span the space, allowing for a simpler representation of the transformation as a diagonal matrix.
馃挕Scalar Multiplication
Scalar multiplication is the operation of multiplying a vector by a scalar, resulting in a new vector that is stretched or compressed along the same line. The video script describes how eigenvectors are affected by linear transformations as a form of scalar multiplication by their corresponding eigenvalues.
馃挕Rotation
Rotation is a type of linear transformation that turns a vector around an axis by a certain angle without changing its length. The script uses rotation as an example to illustrate the concept of finding an eigenvector that remains on its span, which would be the axis of rotation, simplifying the description of the transformation.
馃挕Shear
Shear is a linear transformation that slides every point in a given direction along a line perpendicular to that direction, resulting in a shape that is distorted. The script mentions shear as an example where all vectors along the x-axis are eigenvectors with an eigenvalue of 1, indicating that they remain fixed in place after the transformation.
Highlights

Eigenvectors and eigenvalues are often unintuitive for students, with many questions left unanswered in computations.

Understanding eigenvectors and eigenvalues requires a solid visual understanding of matrices as linear transformations, determinants, linear systems of equations, and change of basis.

Special vectors that remain on their span during a transformation are called eigenvectors, and the factor by which they are stretched or squished is called an eigenvalue.

An eigenvector for a three-dimensional rotation identifies the axis of rotation, simplifying the understanding of the transformation.

Eigenvectors and eigenvalues provide a better understanding of what a linear transformation does, beyond reading matrix columns.

Finding eigenvectors and eigenvalues involves solving the equation A*v = lambda*v, where A is a matrix, v is an eigenvector, and lambda is an eigenvalue.

The expression A*v = lambda*v can be rewritten to A - lambda*I times v = 0, where I is the identity matrix, making it easier to solve.

Eigenvalues are found by setting the determinant of A - lambda*I to zero and solving for lambda.

Not all transformations have eigenvectors, as shown in the example of a 90-degree rotation, which has no real eigenvectors.

A shear transformation can have eigenvectors with eigenvalue 1, such as vectors on the x-axis in the example provided.

A matrix that scales all vectors by the same factor has every vector as an eigenvector with the corresponding eigenvalue.

An eigenbasis consists of basis vectors that are also eigenvectors, making matrix operations simpler, especially when the matrix is diagonal.

Changing the coordinate system to an eigenbasis allows for easier computation of matrix powers and other operations.

The concept of an eigenbasis is crucial for simplifying linear algebra problems, though not all transformations have enough eigenvectors to form one.

The final video in the series will cover abstract vector spaces, building on the concepts discussed in this video.

Transcripts
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