सामग्री पर जाएँ
The Lumenary
स्नातक (प्रारंभिक)Algebraपाठ उपलब्ध

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

इस पर आधारित

20 अनिवार्य पूर्वापेक्षाएँ (बिल्कुल नीचे तक)

आप सीखेंगे

  • 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.

परिभाषा

  • 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.

प्रमेय

  • 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.

आम भ्रांतियाँ

  • 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.

आगे ले जाता है

वैकल्पिक खोजें

अधिगम पथ

आगे पढ़ने के लिए

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