StudySmarter: Study help & AI tools

4.5 • +22k Ratings

More than 22 Million Downloads

Free

Eigenvector

Dive deep into the fascinating world of eigenvectors and their broad application in numerous fields, ranging from engineering to quantum mechanics. This comprehensive guide takes you, step by step, from understanding the basics of eigenvector theory to its advanced usage. Unravel the distinct connections of eigenvectors in network analysis and broaden your knowledge with extensive examples and practical calculation techniques. Whether you're expanding your skills or seeking a refresher on this core concept, this guide is an invaluable resource for paving your way towards mathematically modelling with eigenvectors.

Explore our app and discover over 50 million learning materials for free.

- Design Engineering
- Engineering Fluid Mechanics
- Engineering Mathematics
- Acceptance Sampling
- Addition Rule of Probability
- Algebra Engineering
- Application of Calculus in Engineering
- Area under curve
- Basic Algebra
- Basic Derivatives
- Basic Matrix Operations
- Bayes' Theorem
- Binomial Series
- Bisection Method
- Boolean Algebra
- Boundary Value Problem
- CUSUM
- Cartesian Form
- Causal Function
- Centroids
- Cholesky Decomposition
- Circular Functions
- Complex Form of Fourier Series
- Complex Hyperbolic Functions
- Complex Logarithm
- Complex Trigonometric Functions
- Conservative Vector Field
- Continuous and Discrete Random Variables
- Control Chart
- Convergence Engineering
- Convergence of Fourier Series
- Convolution Theorem
- Correlation and Regression
- Covariance and Correlation
- Cramer's rule
- Cross Correlation Theorem
- Curl of a Vector Field
- Curve Sketching
- D'alembert Wave Equation
- Damping
- Derivative of Polynomial
- Derivative of Rational Function
- Derivative of a Vector
- Directional Derivative
- Discrete Fourier Transform
- Divergence Theorem
- Divergence Vector Calculus
- Double Integrals
- Eigenvalue
- Eigenvector
- Engineering Analysis
- Engineering Graphs
- Engineering Statistics
- Euler's Formula
- Exact Differential Equation
- Exponential and Logarithmic Functions
- Fourier Coefficients
- Fourier Integration
- Fourier Series
- Fourier Series Odd and Even
- Fourier Series Symmetry
- Fourier Transform Properties
- Fourier Transform Table
- Gamma Distribution
- Gaussian Elimination
- Half Range Fourier Series
- Higher Order Integration
- Hypergeometric Distribution
- Hypothesis Test for a Population Mean
- Implicit Function
- Improved Euler Method
- Interpolation
- Inverse Laplace Transform
- Inverse Matrix Method
- Inverse Z Transform
- Jacobian Matrix
- Laplace Shifting Theorem
- Laplace Transforms
- Large Sample Confidence Interval
- Least Squares Fitting
- Logic Gates
- Logical Equivalence
- Maths Identities
- Maxima and Minima of functions of two variables
- Maximum Likelihood Estimation
- Mean Value and Standard Deviation
- Method of Moments
- Modelling waves
- Multiple Regression
- Multiple Regression Analysis
- Newton Raphson Method
- Non Parametric Statistics
- Nonlinear Differential Equation
- Nonlinear Regression
- Numerical Differentiation
- Numerical Root Finding
- One Way ANOVA
- P Value
- Parseval's Theorem
- Partial Derivative
- Partial Derivative of Vector
- Partial Differential Equations
- Particular Solution for Differential Equation
- Phasor
- Piecewise Function
- Polar Form
- Polynomial Regression
- Probability Engineering
- Probability Tree
- Quality Control
- RMS Value
- Radians vs Degrees
- Rank Nullity Theorem
- Rank of a Matrix
- Reliability Engineering
- Runge Kutta Method
- Scalar & Vector Geometry
- Second Order Nonlinear Differential Equation
- Simple Linear Regression Model
- Single Sample T Test
- Standard Deviation of Random Variable
- Superposition
- System of Differential Equations
- System of Linear Equations Matrix
- Taylor's Theorem
- Three Way ANOVA
- Total Derivative
- Transform Variables in Regression
- Transmission Line Equation
- Triple Integrals
- Triple Product
- Two Sample Test
- Two Way ANOVA
- Unit Vector
- Vector Calculus
- Wilcoxon Rank Sum Test
- Z Test
- Z Transform
- Z Transform vs Laplace Transform
- Engineering Thermodynamics
- Materials Engineering
- Professional Engineering
- Solid Mechanics
- What is Engineering

Lerne mit deinen Freunden und bleibe auf dem richtigen Kurs mit deinen persönlichen Lernstatistiken

Jetzt kostenlos anmeldenNie wieder prokastinieren mit unseren Lernerinnerungen.

Jetzt kostenlos anmeldenDive deep into the fascinating world of eigenvectors and their broad application in numerous fields, ranging from engineering to quantum mechanics. This comprehensive guide takes you, step by step, from understanding the basics of eigenvector theory to its advanced usage. Unravel the distinct connections of eigenvectors in network analysis and broaden your knowledge with extensive examples and practical calculation techniques. Whether you're expanding your skills or seeking a refresher on this core concept, this guide is an invaluable resource for paving your way towards mathematically modelling with eigenvectors.

You're about to embark on an exciting journey of discovering eigenvectors, fundamental tools in engineering and the wider field of mathematics. So, sit tight and absorb the knowledge you're about to gain.

Let's kick off with what you're dying to know. An eigenvector, in the most basic sense, is a nonzero vector that changes by a scalar factor when a linear transformation is applied to it. Curious to know more? Let's dive deeper.

If you break down the term 'eigenvector', it hails from the German word 'eigen', which means 'own' or 'particular to'. The 'vector' part, on the other hand, is a mathematical term for quantity defined by both magnitude and direction. Hence, you can understand an eigenvector as a specific type of vector that maintains its direction under the effect of a matrix transformation. Phew!

An eigenvector is represented as:

\[ Av = λv \]where \( A \) is the transformation matrix, \( v \) is the eigenvector and \( λ \) is the eigenvalue.

Let's imagine a system of linear equations represented by a 2x2 matrix. By applying transformations, vectors in this space might rotate, reflect, and dilate. But there will always be at least one vector that only dilates and keeps its direction. That's your eigenvector!

You'll be surprised to learn that eigenvectors don't just sit in a maths classroom. They have numerous practical implications across various fields including engineering and network analysis.

In mechanical engineering, eigenvectors play a critical role in studying physical phenomena such as stress. For instance, when a complex stress field is analysed, eigenvectors represent the direction of principal stresses. Even in the field of electrical engineering, eigenvectors and eigenvalues are used in the analysis and design of systems and signals. The topic is deep and insightful, providing a fundamental base in engineering tasks.

Imagine you wish to study connectivity in an online social network. Each user is a node and their interactions define the edges. So, who's the most influential individual? Here's where eigenvector centrality comes in. By assigning more value to nodes connected to high-scoring nodes, this method uses eigenvectors to calculate the influence of each individual in the network.

As you tread further into the world of eigenvectors, you'll come across a unique class known as orthogonal eigenvectors. Sound interesting? Let's get started.

Orthogonal eigenvectors are eigenvectors that are perpendicular to each other in the Euclidean space. In other words, the dot product of orthogonal eigenvectors equals zero. Essentially, they represent the uncorrelation of dimensions in your space. The uncoupling simplifies many problems in data analysis, making orthogonal eigenvectors a key concept in multivariate analysis.

Let's consider a 2D plane. If you have two vectors at a 90-degree angle to each other, they are orthogonal. Now, combine this with the principle of scaling upon transformation, and you have orthogonal *eigen*vectors! Aren't they intriguing?

Mathematically modelling with eigenvectors is a crucial concept in multiple scientific fields. From physics to computer graphics, these special types of vector contribute significantly. They help in efficiently representing and analysing dynamic systems and physical processes. So, how do you calculate these handy mathematical tools? Let's explore this in detail.

Calculating an eigenvector involves a step-by-step process that requires understanding of matrix algebra and fundamental linear algebra concepts. The process may appear tricky at first, but with practice and understanding, calculating eigenvectors can become second nature. Here's the generic methodology to calculate eigenvectors from a given matrix.

Briefly summarised, here's the process for calculating eigenvectors:

- Identify the target matrix \( A \).
- Calculate the roots \( \lambda \) of the characteristic equation \(| A - \lambda I | = 0\).
- For each root \( \lambda \), solve \( (A - \lambda)I v = 0 \) for \( v \).
- Each solved \( v \) represents another eigenvector of the matrix \( A \).

Let's consider the matrix \(A = \begin{pmatrix} 4 & 1 \\ 2 & 3 \end{pmatrix}\) We calculate the roots of the characteristic equation \( | A - \lambda I | = 0 \). Here, \( I \) is the identity matrix. This gives us the two roots or eigenvalues \( \lambda_{1} = 5 \) and \( \lambda_{2} = 2 \). Next, we substitute \( \lambda \) back into the equation and solve for \( v \). For \( \lambda_{1} = 5 \), by solving the equation, we get \( v_{1} = \begin{pmatrix} 1 \\ -1 \end{pmatrix}\) Similarly, for \( \lambda_{2} = 2 \), we get \( v_{2} = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\) Hence, the two eigenvectors for given matrix are \( v_{1} \) and \( v_{2} \).

As you delve deeper into working with matrices, you realise that calculating eigenvectors can be computationally intensive, especially for large matrices. Fear not, there are techniques that can help speed up this process. Let's explore these.

There are some key steps that you can integrate to speed up eigenvector computations:

**Reducing to Hessenberg form:**This involves reducing the original matrix to a simpler Hessenberg matrix through similarity transform, which retains the eigenvalues but simplifies the process.**Applying the QR algorithm:**Once in Hessenberg form, the QR algorithm is used, dividing the process into a sequence of easy-to-compute orthogonal transformations.**Utilising divide-and-conquer approach:**This technique permits dividing larger problem into smaller ones and solving them individually, resulting in a significant reduction in computation times for larger matrices.

As you advance in your journey with eigenvectors, high-level applications in areas such as differential equations and quantum mechanics start to unfold. Intriguing, isn't it? Read on to discover how eigenvectors are embedded into these complex scientific paradigms.

When it comes to computational tasks and complex calculations, differential equations can often seem like challenging territories. That's where the concept of eigenvectors comes in. Yes, you heard it right! Eigenvectors aren't limited to linear algebra; they extend their practicality to differential equations as well. From simplifying complex problems to adding a mathematical flair to the solutions, eigenvectors truly stretch their dash.

Eigenvectors have a special property; they remain in their own span during linear transformation. Taking advantage of this, we use eigenvectors in the study of linear differential equations. Specifically, in solving systems of linear differential equations, the eigenvalues and eigenvectors of the associated coefficient matrix can provide a shortcut to finding the general solution.

Assuming a homogeneous system of linear differential equations, the system often reads: \(x' = Ax\), where \(A\) is the coefficient matrix and \(x\) is the vector of dependent variables. The general solution is typically of the form: \(x(t) = c_1e^{\lambda_1 t}v_1 + c_2e^{\lambda_2 t}v_2 + ... + c_ne^{\lambda_n t}v_n\), where \( \lambda_n \) and \(v_n\) are the eigenvalues and corresponding eigenvectors of \(A\), and \(c_n\) are arbitrary constants.

This scheme achieves the reduction of a system of differential equations into isolated equations, simplifying the calculation task at hand.

Let’s consider a system of two differential equations:

\( \frac{dx_1}{dt} = 4x_1 + x_2 \) \( \frac{dx_2}{dt} = 2x_1 + 3x_2 \)

This can be represented in matrix form as:

\( \frac{d}{dt}\begin{pmatrix}x_1 \\ x_2\end{pmatrix} = \begin{pmatrix}4 & 1 \\ 2 & 3\end{pmatrix}\begin{pmatrix}x_1 \\ x_2\end{pmatrix} \)

Solving for the eigenvalues \(\lambda\) of the matrix, we find that \(\lambda_{1} = 5\) and \(\lambda_{2} = 2\). The associated eigenvectors are \(v_{1} = \begin{pmatrix} 1 \\ -1 \end{pmatrix}\) and \(v_{2} = \begin{pmatrix} -1 \\ 2 \end{pmatrix}\), respectively. Hence, the general solution to this system of differential equations is \(x(t) = c_1e^{5t}v_1 + c_2e^{2t}v_2\).

From the classical to the quantum, the power of the eigenvector is far-reaching. In quantum mechanics, a scientific discipline dealing with the peculiarities of tiny subatomic particles, eigenvectors are prominently employed, particularly in the concept of quantum states and observables. Let's unravel these intriguing connections.

In quantum mechanics, the state of a system is described by a wave function, or state vector, which belongs to a Hilbert space. This is an abstract vector space, equipped with the structure of an inner product that allows length and angle to be measured. The observable quantities in a quantum system are represented by linear operators acting on these state vectors.

This is where eigenvectors and eigenvalues come in. The eigenvalues of an operator correspond to the possible values that you can measure for that corresponding observable. Meanwhile, the eigenvectors of the operator, or eigenstates, represent the specific states of the system in which the observable has a certain value.

For a given operator \(\hat{O}\), the associated eigenvalues and eigenvectors satisfy \(\hat{O}|\psi\rangle = \lambda|\psi\rangle\), where \(|\psi\rangle\) represents an eigenstate (eigenvector) and \(\lambda\) is the associated observable (eigenvalue).

A classic example in quantum mechanics is the position operator in one dimension. The position operator \(\hat{x}\) acting on the state \(|x\rangle\) gives \(\hat{x}|x\rangle = x|x\rangle\). Hence, the eigenvalue \(x\) gives the position of the particle, and the state \(|x\rangle\) indicates that the particle is definitely at position \(x\).

In another example, the energy levels of an electron in a hydrogen atom are calculated by solving the time-independent Schrödinger equation: \[\hat{H}|\psi\rangle = E|\psi\rangle\]. Here, \(\hat{H}\) is the Hamiltonian operator, \(|\psi\rangle\) is the quantum state, and \(E\) is the energy of the state. Solving this equation yields the possible energy levels and associated states of the electron.

- An eigenvector is a nonzero vector that undergoes a scalar transformation when a linear transformation is applied to it.
- The eigenvector equation is represented as
*Av = λv*, where*A*is the transformation matrix,*v*is the eigenvector, and*λ*is the eigenvalue. - Eigenvectors have diverse applications in various fields such as engineering, network analysis, and quantum mechanics, particularly in studying physical phenomena, network influence, and quantum states, respectively.
- Orthogonal eigenvectors are eigenvectors that are perpendicular to each other in Euclidean space, representing uncorrelated dimensions; this simplifies many problems in data analysis.
- Calculating eigenvectors involves linear algebra, using matrix algebra. To speed up computation, techniques like reducing to Hessenberg form, applying the QR algorithm, and a divide-and-conquer approach can be used.

An eigenvector is a non-zero vector that only changes by an overall scale when a corresponding linear transformation is applied to it. This change is characterised by the eigenvalue, a scalar indicating the factor by which the eigenvector is scaled.

To find an eigenvector, start by solving the characteristic equation (det(A - λI) = 0) of a square matrix to find the eigenvalues (λ). Then, for each eigenvalue, substitute it back into the equation (A - λI)v = 0 to find the corresponding eigenvectors (v).

Eigenvectors are not necessarily orthogonal. However, for a symmetric matrix, its eigenvectors can be chosen to be orthogonal. This is a consequence of the spectral theorem in linear algebra.

Yes, eigenvectors corresponding to different eigenvalues of a matrix are always linearly independent. This is a fundamental property in linear algebra that is widely utilised in the field of engineering.

To find the eigenvectors of a 3x3 matrix, first find the eigenvalues by setting the determinant of (A - λI) equal to zero, where A is the matrix, λ is a scalar, and I is the identity matrix. Next, for each eigenvalue, find the null space of the matrix (A - λI), these null spaces are the corresponding eigenvectors.

What's the fundamental definition of an eigenvector?

An eigenvector is a nonzero vector that changes by a scalar factor when a linear transformation is applied to it.

What is the real-world applications of eigenvectors in engineering?

In engineering, eigenvectors are used to study physical phenomena like stress. In electrical engineering, they are used in the analysis and design of systems and signals.

What is the eigenvalue in the equation used to represent an eigenvector (Av = λv)?

In the equation Av = λv, λ represents the eigenvalue.

What are orthogonal eigenvectors and their significance?

Orthogonal eigenvectors are those that are perpendicular to each other in Euclidean space. They represent the uncorrelation of dimensions in a space and are key in multivariate analysis.

What is the process for calculating eigenvectors?

The process involves identifying the target matrix, calculating the roots of the characteristic equation, solving for each root, and each solved value is an eigenvector.

What are eigenvectors and how are they used?

Eigenvectors are special types of vectors used in multiple scientific fields to efficiently represent and analyse dynamic systems and physical processes.

Already have an account? Log in

Open in App
More about Eigenvector

The first learning app that truly has everything you need to ace your exams in one place

- Flashcards & Quizzes
- AI Study Assistant
- Study Planner
- Mock-Exams
- Smart Note-Taking

Sign up to highlight and take notes. It’s 100% free.

Save explanations to your personalised space and access them anytime, anywhere!

Sign up with Email Sign up with AppleBy signing up, you agree to the Terms and Conditions and the Privacy Policy of StudySmarter.

Already have an account? Log in