SimilarityTransform - crowlogic/arb4j GitHub Wiki

In linear algebra, similarity transformations are applied to both real-valued and complex-valued square matrices. The theory applies broadly to matrices over any field, but the most common contexts are real and complex numbers. Let's delve into the mathematical details:

Definition and Basic Formula

  • A matrix $B$ is said to be similar to a matrix $A$ if there exists an invertible matrix $P$ such that:

$$ B = P^{-1}AP $$

  • Here, $A, B$ are $n \times n$ matrices, and $P$ is an $n \times n$ invertible matrix.

Properties and Implications

  1. Eigenvalues:

    • Eigenvalues remain invariant under similarity transformations. If $\lambda$ is an eigenvalue of $A$, it is also an eigenvalue of $B$.
    • This is because, if $Av = \lambda v$ for some vector $v$, then for $w = Pv$, we have $Bw = \lambda w$.

  2. Change of Basis:

    • $P$ represents the matrix that changes basis vectors. The columns of $P$ are the new basis vectors in the original basis.
    • In this context, the transformation $B = P^{-1}AP$ can be seen as representing the same linear transformation as $A$ but in a different basis.

  3. Diagonalization:

    • A matrix $A$ is diagonalizable if it is similar to a diagonal matrix $D$. So, $A = PDP^{-1}$ for some invertible matrix $P$.
    • Diagonalization is particularly useful because it simplifies many computations, but not all matrices are diagonalizable.

  4. Jordan Canonical Form:

    • For matrices that can't be diagonalized, the Jordan canonical form provides a nearly diagonal form.
    • In this form, a matrix $A$ is transformed into a block diagonal matrix where each block is a Jordan block, a special type of matrix associated with the eigenvalue of $A$.

Real-Valued vs. Complex-Valued Matrices

  • The theory applies to both real and complex matrices. However, the nature of the field can affect the outcomes. For instance:
    • Over the complex numbers, every matrix has a full set of eigenvalues, thanks to the Fundamental Theorem of Algebra. This is not always true over the real numbers.
    • Some matrices might only be diagonalizable over the complex field and not over the reals.

Applications

  • In quantum mechanics, similarity transformations are used to study linear operators in Hilbert spaces, often with complex-valued matrices.
  • In control theory and systems analysis, real-valued matrices are often transformed to simplify the analysis of system dynamics.

In conclusion, similarity transformations in linear algebra involve changing the basis of a vector space to transform matrices while preserving their essential properties like eigenvalues. This concept applies to matrices over any field, with widespread applications in various branches of mathematics and science.