Matrix Rank and Nullity:
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The rank of a matrix is the dimension of its row space (or column space), while the nullity is the dimension of its kernel (null space). Together, they satisfy the Rank-Nullity Theorem: Rank(A) + Nullity(A) = n, where n is the number of columns in matrix A.
The calculator analyzes the matrix structure and identifies variables that affect the rank and nullity:
For matrices with variables, the calculator shows how the rank and nullity depend on these variables.
Details: Rank determines the number of linearly independent equations in a system, while nullity indicates the number of free variables in solutions. They are fundamental in solving linear systems and understanding linear transformations.
Tips: Enter the matrix with one row per line, elements separated by spaces. Use letters for variables. The calculator will show how rank and nullity depend on these variables.
Q1: What's the difference between row rank and column rank?
A: For any matrix, row rank equals column rank, so we just refer to "rank".
Q2: How do variables affect rank and nullity?
A: Depending on variable values, rows may become linearly dependent, reducing rank and increasing nullity.
Q3: What's the maximum possible rank for an m×n matrix?
A: The maximum rank is min(m, n) - the smaller of row or column count.
Q4: When does a matrix have nullity zero?
A: When the matrix has full column rank (all columns are linearly independent).
Q5: How is this related to solving Ax=b?
A: The nullity gives the dimension of the homogeneous solution space, while rank determines if solutions exist.