SUMMARY
The discussion centers on proving that a symmetric matrix of rank one, denoted as \( C \), can be expressed in the form \( C = aww^T \), where \( a \) is a scalar and \( w \) is a unit vector. The proof begins by establishing that if \( C \) has rank one, its range space is one-dimensional, leading to the conclusion that \( Cw \) is a scalar multiple of \( w \). The symmetry of \( C \) is confirmed through the relationship \( w^TC = aw^T \), ultimately demonstrating that \( C \) can be represented as \( aww^T \) for any vector \( x \).
PREREQUISITES
- Understanding of symmetric matrices
- Knowledge of matrix rank and its implications
- Familiarity with vector norms and unit vectors
- Basic linear algebra concepts, including matrix multiplication
NEXT STEPS
- Study the properties of symmetric matrices in linear algebra
- Explore the concept of matrix rank and its significance in linear transformations
- Learn about the Singular Value Decomposition (SVD) and its relation to matrix rank
- Investigate applications of rank-one matrices in data science and machine learning
USEFUL FOR
Mathematicians, students of linear algebra, and professionals working with matrix computations will benefit from this discussion, particularly those interested in the properties of symmetric matrices and their applications in various fields.