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The Lumenary
Primeros años de universidadAlgebraPlanificado

Linear algebra

Systems of linear equations and Gaussian elimination, matrices, vector spaces, linear maps, bases and dimension, rank–nullity, determinants, eigenvalues and diagonalisation.

math.algebra.linear-algebra

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Requisitos previos obligatorios, hasta el fondo: 20

Aprenderás a

  • Solve linear systems by Gaussian elimination and describe solution sets as translates of null spaces.
  • Prove that all bases of a finite-dimensional vector space have the same size, and apply the rank–nullity theorem.
  • Represent linear maps by matrices, compose them by matrix multiplication and change basis.
  • Compute eigenvalues and eigenvectors and decide whether a matrix is diagonalisable.

Definición

  • A vector space over a field k is a set with addition and scalar multiplication satisfying the vector-space axioms.
  • A linear map T : V → W satisfies T(u + v) = T(u) + T(v) and T(cv) = cT(v).
  • A basis is a linearly independent spanning set; dim V is the number of elements in a basis.
  • λ is an eigenvalue of T if Tv = λv for some non-zero vector v.

Teorema

  • Rank–nullity: dim V = dim ker T + dim im T for a linear map T : V → W with V finite-dimensional.
  • A composite of linear maps is linear, and corresponds to the product of their matrices.
  • A square matrix is invertible if and only if its determinant is non-zero, if and only if its columns are linearly independent.
  • Eigenvectors belonging to distinct eigenvalues are linearly independent.

Errores frecuentes

  • Thinking matrix multiplication is commutative.
  • Assuming every square matrix is diagonalisable; the matrix with rows (0, 1) and (0, 0) is not.
  • Believing a linear map must send lines through the origin to lines; it may collapse them to a point.

Conduce a

Descubrimientos opcionales

Itinerarios de aprendizaje

Lecturas complementarias

  • Sheldon Axler, Linear Algebra Done Right (1996)
  • Gilbert Strang, Introduction to Linear Algebra