Linear Algebra
Offered By: NPTEL via YouTube
Course Description
Overview
COURSE OUTLINE: In this course, you will learn systems of linear equations, Matrices, Elementary row operations, Row-reduced echelon matrices. Vector spaces, Subspaces, Bases and dimension, Ordered bases and coordinates. Linear transformations, Rank-nullity theorem, Algebra of linear transformations, Isomorphism, Matrix representation, Linear functionals, Annihilator, Double dual, Transpose of a linear transformation. Characteristic values and characteristic vectors of linear transformations, Diagonalizability, Minimal polynomial of a linear transformation, Cayley-Hamilton theorem, Invariant subspaces, Direct-sum decompositions, Invariant direct sums, The primary decomposition theorem, Cyclic subspaces and annihilators, Cyclic decomposition, Rational, Jordan forms. Inner product spaces, Orthonormal bases, Gram-Schmidt process.
Syllabus
Mod-01 Lec-01 Introduction to the Course Contents..
Mod-01 Lec-02 Linear Equations.
Mod-01 Lec-03a Equivalent Systems of Linear Equations I: Inverses of Elementary Row-operations.
Mod-01 Lec-03b Equivalent Systems of Linear Equations II: Homogeneous Equations, Examples.
Mod-01 Lec-04 Row-reduced Echelon Matrices.
Mod-01 Lec-05 Row-reduced Echelon Matrices and Non-homogeneous Equations.
Mod-01 Lec-06 Elementary Matrices, Homogeneous Equations and Non-homogeneous Equations.
Mod-01 Lec-07 Invertible matrices, Homogeneous Equations Non-homogeneous Equations.
Mod-02 Lec-08 Vector spaces.
Mod-02 Lec-09 Elementary Properties in Vector Spaces. Subspaces.
Mod-02 Lec-10 Subspaces (continued), Spanning Sets, Linear Independence, Dependence.
Mod-03 Lec-11 Basis for a vector space.
Mod-03 Lec-12 Dimension of a vector space.
Mod-03 Lec-13 Dimensions of Sums of Subspaces.
Mod-04 Lec-14 Linear Transformations.
Mod-04 Lec-15 The Null Space and the Range Space of a Linear Transformation.
Mod-04 Lec-16 The Rank-Nullity-Dimension Theorem. Isomorphisms Between Vector Spaces.
Mod-04 Lec-17 Isomorphic Vector Spaces, Equality of the Row-rank and the Column-rank I.
Mod-04 Lec-18 Equality of the Row-rank and the Column-rank II.
Mod-05 Lec19 The Matrix of a Linear Transformation.
Mod-05 Lec-20 Matrix for the Composition and the Inverse. Similarity Transformation.
Mod-06 Lec-21 Linear Functionals. The Dual Space. Dual Basis I.
Mod-06 Lec-22 Dual Basis II. Subspace Annihilators I.
Mod-06 Lec-23 Subspace Annihilators II.
Mod-06 Lec-24 The Double Dual. The Double Annihilator.
Mod-06 Lec-25 The Transpose of a Linear Transformation. Matrices of a Linear.
Mod-07 Lec-26 Eigenvalues and Eigenvectors of Linear Operators.
Mod-07 Lec-27 Diagonalization of Linear Operators. A Characterization.
Mod-07 Lec-28 The Minimal Polynomial.
Mod-07 Lec-29 The Cayley-Hamilton Theorem.
Mod-08 Lec-30 Invariant Subspaces.
Mod-08 Lec-31 Triangulability, Diagonalization in Terms of the Minimal Polynomial.
Mod-08 Lec-32 Independent Subspaces and Projection Operators.
Mod-09 Lec-33 Direct Sum Decompositions and Projection Operators I.
Mod-09 Lec-34 Direct Sum Decomposition and Projection Operators II.
Mod-10 Lec-35 The Primary Decomposition Theorem and Jordan Decomposition.
Mod-10 Lec-36 Cyclic Subspaces and Annihilators.
Mod-10 Lec-37 The Cyclic Decomposition Theorem I.
Mod-10 Lec-38 The Cyclic Decomposition Theorem II. The Rational Form.
Mod-11 Lec-39 Inner Product Spaces.
Mod-11 Lec-40 Norms on Vector spaces. The Gram-Schmidt Procedure I.
Mod-11 Lec-41 The Gram-Schmidt Procedure II. The QR Decomposition..
Mod-11 Lec-42 Bessel's Inequality, Parseval's Indentity, Best Approximation.
Mod-12 Lec-43 Best Approximation: Least Squares Solutions.
Mod-12 Lec-44 Orthogonal Complementary Subspaces, Orthogonal Projections.
Mod-12 Lec-45 Projection Theorem. Linear Functionals.
Mod-13 Lec-46 The Adjoint Operator.
Mod-13 Lec-47 Properties of the Adjoint Operation. Inner Product Space Isomorphism.
Mod-14 Lec-48 Unitary Operators.
Mod-14 Lec-49 Unitary operators II. Self-Adjoint Operators I..
Mod-14 Lec-50 Self-Adjoint Operators II - Spectral Theorem.
Mod-14 Lec-51 Normal Operators - Spectral Theorem.
Taught by
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