In This Lesson Linear Transformations Vector Spaces & Subspaces Basis & Dimension 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 Rank & Nullity A linear transformation T: V → W satisfies:
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T(u + v) = T(u) + T(v) (additivity)
T(cv) = cT(v) (homogeneity)
Every linear transformation from ℝⁿ to ℝᵐ can be represented as multiplication by an m×n matrix . Geometric examples:
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A subspace is a subset closed under addition and scalar multiplication. Important subspaces of a matrix A: column space (Col A), row space, null space (Nul A).
Basis & Dimension
A basis is a linearly independent spanning set.
dim(V) = number of vectors in any basis
dim(ℝⁿ) = n, with standard basis e₁, e₂, …, eₙ
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Rank & Nullity
rank(A) = dim(Col A) = dim(Row A)
nullity(A) = dim(Nul A)
Rank-Nullity Theorem: rank(A) + nullity(A) = n
The Rank-Nullity Theorem is a dimension-counting result: the "input space" ℝⁿ splits into the part that maps to nonzero outputs (rank) and the part that maps to zero (nullity). This connects to solution counts for
systems of equations : unique (full rank), infinite (nullity > 0), or none (inconsistent).
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