Eigenvectors

Definition

Eigenvectors are non-zero vectors that change only by a scalar factor when a linear transformation is applied to them. They are key in understanding how systems evolve over time, particularly in systems of differential equations and stability analysis, as they help identify invariant directions under transformation and indicate the stability characteristics of equilibria.

5 Must Know Facts For Your Next Test

1. Eigenvectors are found by solving the equation $A\mathbf{v} = \lambda\mathbf{v}$, where $A$ is a matrix, $\lambda$ is an eigenvalue, and $\mathbf{v}$ is an eigenvector.
2. In the context of differential equations, eigenvectors correspond to modes of behavior in dynamic systems, revealing how solutions evolve over time.
3. A system's stability can be determined by analyzing its eigenvalues: if all eigenvalues have negative real parts, the system is stable; if any have positive real parts, it is unstable.
4. Eigenvectors can be normalized to have unit length, making them easier to work with while preserving their directionality in the analysis.
5. Multiple eigenvectors can correspond to the same eigenvalue, especially in cases of repeated eigenvalues, which can lead to complex dynamics in the system.

Review Questions

• How do eigenvectors relate to the behavior of solutions in systems of differential equations?
  • Eigenvectors represent specific directions in which the solutions to differential equations evolve under linear transformations. Each eigenvector corresponds to an eigenvalue, which indicates how much the solution grows or decays over time. By analyzing these eigenvectors and their corresponding eigenvalues, we can determine the long-term behavior of the system, including whether it converges to an equilibrium point or diverges.
• Discuss the implications of having complex eigenvalues and their associated eigenvectors for the stability of a dynamical system.
  • Complex eigenvalues indicate that the system has oscillatory behavior. When a dynamical system has complex eigenvalues with non-zero imaginary parts, the corresponding eigenvectors suggest that solutions will oscillate as they evolve. The real part of these complex eigenvalues determines whether these oscillations grow or decay over time. If the real part is negative, oscillations will eventually die out and the system will stabilize; if positive, oscillations will grow and lead to instability.
• Evaluate how changes in a matrix affect its eigenvectors and their role in stability analysis.
  • Changes in a matrix can significantly impact its eigenvectors and thus alter the stability characteristics of the corresponding dynamical system. When parameters in a matrix change, new eigenvectors may emerge or existing ones may change direction or magnitude. This directly influences how the system behaves near equilibrium points—if new eigenvalues appear with positive real parts due to these changes, it could result in previously stable equilibria becoming unstable. Thus, understanding how eigenvectors respond to changes in the system is critical for predicting stability outcomes.

Related terms

Eigenvalues: Eigenvalues are scalars associated with eigenvectors that represent the factor by which the corresponding eigenvector is stretched or compressed during a linear transformation.

Linear Transformation: A linear transformation is a mapping between two vector spaces that preserves the operations of vector addition and scalar multiplication, often represented by matrices.

Stability:

Stability refers to the behavior of a system's equilibrium points over time, determining whether small perturbations will grow or decay, often analyzed using eigenvalues and eigenvectors.