Difference between revisions of "MAT5283"

From Department of Mathematics at UTSA
Jump to navigation Jump to search
 
(7 intermediate revisions by the same user not shown)
Line 1: Line 1:
(1) Finite-dimensional vector spaces: Vector space axioms, subspaces, linear independence and bases, dimension, sums and quotients of vector spaces
 
(2) Linear transformations: Rank and nullity, isomorphisms, bases, change of basis, canonical forms. (3) Inner product spaces: Norm, angles, orthogonal and orthonormal sets, orthogonal complement, best approximation and least squares, Riesz Representation Theorem. (4) Determinants: Axiomatic characterization of determinants, multilinear functions, existence and uniqueness of determinants. (5) Eigenvalues and eigenvectors: Characteristic polynomials, eigenspaces. (6) Block diagonal representation: Diagonalizability, triangularizability, block representation, Jordan normal form. (6) The Spectral Theorem.
 
 
 
Introduction to the theory of finite-dimensional vector spaces.  
 
Introduction to the theory of finite-dimensional vector spaces.  
 
'''Sample textbook''':
 
 
[1] M. Thamban Nair · Arindama Singh, ''Linear Algebra'', 2008. Freely available to UTSA students.
 
 
 
  
 
'''Catalog entry'''
 
'''Catalog entry'''
  
''Prerequisite'': Algebra and Number Systems (MAT 1313), or Discrete Mathematical Structures (CS 2233/2231), or instructor consent.
+
''Prerequisite'': Prerequisite: Math 2233 Linear Algebra, Math 2243 Applied Linear Algebra or instructor approval.  , or instructor consent.
 
 
''Contents''
 
(1) Finite-dimensional vector spaces: Vector space axioms, subspaces, linear independence and bases, dimension, sums and quotients of vector spaces
 
(2) Linear
 
(3) Graph models: Isomorphisms, edge counting, planar graphs.
 
(4) Covering circuits and graph colorings: Euler circuits, Hamilton circuits, graph colorings, Ramsey's theorem
 
(5) Network algorithms: Shortest path, minimum spanning trees, matching algorithms, transportation problems.
 
(6) Order relations: Partially ordered sets, totally ordered sets, extreme elements (maximum, minimum, maximal and minimal elements), well-ordered sets, maximality principles.
 
 
 
 
 
 
 
 
 
 
 
 
 
  
==Topics List==
+
''Contents'':
{| class="wikitable sortable"
+
(1) Algebraic Structures: rings, matrices, polynomials, fields and finite fields (2) Vector
! Week !! Topic !! Sections from the Nair-Singh book !! Subtopics !! Prerequisite
+
spaces: subspaces, linear independence, basis, dimension (3) Linear transformations, isomor-
|-
+
phisms, Rank-Nullity Theorem, Product and Quotient Spaces (3) Matrices and Linear Transfor-
1-3 
+
mations: Matrix representation, change of basis, equivalence of matrices, similarity (4) Eigen-
|| [[Finite-dimensional vector spaces]]
+
values and Eigenvectors: The Cayley-Hamilton Theorem, Diagonalization and Jordan Canoni-
|| 1.1-1.8
+
cal forms (4) Real and Complex Inner Product Spaces: Norm, distances, Cauchy-Schwartz in-
|| Vector space axioms, subspaces, linear independence and bases, dimension, sums and quotients of vector spaces
+
equality, Gram-Schmidt Orthogonalization process, The Projection Theorem, Riez Representa-
|| MAT1313, CS2233/2231, or instructor consent.
+
tion Theorem (5) Orthogonality over finite fields (6) Multilinear algebra : Bilinear Forms and
|-
+
Functionals, Tensor product of vector spaces and Matrices.
|  4-5 
 
|| [[Linear transformations]]
 
|| 2.1-2.6
 
|| Rank and nullity, matrix representation, the space of linear transformations.
 
|-
 
|  6 
 
|| [[Gauss-jordan elimination]]
 
|| 3.1-3.7
 
|| Row operations, echelon form and reduced echelon form, determinants.
 
|-
 
|  7-
 
|| [[Inner product spaces]]
 
|| 4.1-4.8
 
|| Projections, orthogonal bases and Gram-Schmidt, least squares approximation, Riesz representation.
 
|-
 
|  9 
 
|| [[Eigenvalues and eigenvectors]]
 
|| 5.1-5.5
 
|| Eigenspaces, characteristic polynomials
 
|-
 
|  10 
 
|| [[Canonical forms]]
 
|| 6.1-6.5
 
|| Jordan form
 
|-
 
|  11-13 
 
|| [[Spectral representation]]
 
|| 7.1-7.6
 
|| Singular value and polar decomposition.
 
|}
 

Latest revision as of 19:48, 24 March 2026

Introduction to the theory of finite-dimensional vector spaces.

Catalog entry

Prerequisite: Prerequisite: Math 2233 Linear Algebra, Math 2243 Applied Linear Algebra or instructor approval. , or instructor consent.

Contents: (1) Algebraic Structures: rings, matrices, polynomials, fields and finite fields (2) Vector spaces: subspaces, linear independence, basis, dimension (3) Linear transformations, isomor- phisms, Rank-Nullity Theorem, Product and Quotient Spaces (3) Matrices and Linear Transfor- mations: Matrix representation, change of basis, equivalence of matrices, similarity (4) Eigen- values and Eigenvectors: The Cayley-Hamilton Theorem, Diagonalization and Jordan Canoni- cal forms (4) Real and Complex Inner Product Spaces: Norm, distances, Cauchy-Schwartz in- equality, Gram-Schmidt Orthogonalization process, The Projection Theorem, Riez Representa- tion Theorem (5) Orthogonality over finite fields (6) Multilinear algebra : Bilinear Forms and Functionals, Tensor product of vector spaces and Matrices.