SUMMARY
The rank of a matrix is defined as the number of linearly independent rows or columns within that matrix. To determine the rank, one must perform Gaussian elimination on the matrix and count the number of non-zero rows in the resulting echelon form. For example, the matrix A = [[2, 3, -2], [2, 6, 0], [-4, 0, 0]] has a rank of 3, while the matrix [[1, 2], [6, -3]] has a rank of 2. The rank indicates the number of columns that cannot be expressed as a linear combination of others.
PREREQUISITES
- Understanding of matrix notation and operations
- Familiarity with Gaussian elimination
- Knowledge of linear independence and linear combinations
- Basic concepts of vector spaces
NEXT STEPS
- Study Gaussian elimination techniques in detail
- Learn about linear independence and its implications in vector spaces
- Explore the concept of spanning sets in linear algebra
- Investigate applications of matrix rank in solving linear systems
USEFUL FOR
Students of linear algebra, mathematicians, and anyone seeking to understand matrix theory and its applications in various fields such as engineering and computer science.