**Eigenvalues** are a pivotal concept in linear algebra, heavily influencing fields such as quantum mechanics, stability analysis, and more. They represent the factors by which a transformation can stretch or compress vectors.
If \(A\) is a linear operator, a scalar \(\lambda\) is considered an eigenvalue of \(A\) if there exists a non-zero vector \(x\) such that \(Ax = \lambda x\). Here, \(x\) is known as an eigenvector corresponding to the eigenvalue \(\lambda\).
Some important aspects of eigenvalues include:
- They indicate whether a matrix (or linear operator) is invertible: if 0 is not an eigenvalue, the operator is invertible.
- For self-adjoint operators, eigenvalues are always real numbers. This property is vital in physics, particularly in quantum mechanics, where physical states are represented by such operators.
- The set of all eigenvalues of an operator is called its spectrum, illustrating the operator's overall transformation characteristics.
Eigenvalues provide a window into understanding the deeper properties of operators and transformations, making them indispensable across numerous scientific and mathematical disciplines.