Linear Algebra (MTH 288)
- Page ID
- 228670
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- 1: Systems of Linear Equations
- This page introduces the concept of solutions to systems of linear equations, emphasizing their applications in fields like biology and engineering. It outlines the systematic techniques of linear algebra for solving these equations, including Gaussian elimination and homogeneous equations. The content sets the foundation for further study and includes applications in network flow, electrical networks, and chemical reactions, complemented by practice exercises.
- 2: Matrix Algebra
- This page transitions from solving systems of linear equations to an in-depth exploration of matrices, emphasizing their properties and applications in areas like geometric transformations. It covers elementary matrices, matrix operations (addition, scalar multiplication, transposition), multiplication, inverses, and concepts such as linear transformations and LU-factorization.
- 3: Determinants and Diagonalization
- This page explores the concept of determinants in square matrices, highlighting their importance for matrix invertibility, inverses, and eigenvalues, which aid in diagonalization and system behavior prediction. It discusses their historical background, from Leibnitz's early studies to Gauss's terminology, and notes their ongoing relevance in matrix algebra, despite being less prominent.
- 4: Vector Geometry
- This page introduces 3-dimensional geometry by representing points as vectors from the origin, building on 2D concepts. It covers transformations like rotations and reflections, focusing on perpendicular lines and their significance. Various exercises on vectors, projections, and their applications in computer graphics are provided. The chapter culminates in linear operators in \(\mathbb{R}^3\) and includes additional exercises for further practice.
- 5: Vector Space Rⁿ
- This page covers essential linear algebra concepts like subspaces, spanning sets, and vector independence, emphasizing unique representations. It also introduces orthogonality, matrix rank, similarity, diagonalization, and linear approximations. The section highlights least squares methods for finding best approximations in problems lacking exact solutions, with applications in correlation and variance.
- 6: Vector Spaces
- This page introduces vector spaces, expanding on \(\mathbb{R}^n\) and focusing on their abstract nature, defined by sets of objects (vectors) that can be added and scaled according to specific rules. It discusses the historical development of vector spaces, recognizing influential figures like Hermann Grassmann and Guiseppe Peano. The chapter aims to help readers transition smoothly to more abstract vector systems by using familiar concepts.
- 6.1: Prelude to Vector Spaces
- 6.2: Examples and Basic Properties
- 6.3: Subspaces and Spanning Sets
- 6.4: Linear Independence and Dimension
- 6.5: Finite Dimensional Spaces
- 6.6: An Application to Polynomials
- 6.7: Linear Transformations of Vector Spaces
- 6.8: Kernel and Image of a Linear Transformation
- 6.E: Supplementary Exercises for Chapter 6
- 7: Applications
- 7.1: An Application to Electrical Networks
- 7.2: An Application to Input-Output Economic Models
- 7.3: An Application to Markov Chains
- 7.4: An Application to Network Flow
- 7.5: An Application to Chemical Reactions
- 7.6: An Application to Computer Graphics
- 7.7: An Application to Linear Recurrences
- 7.8: An Application to Systems of Differential Equations
- 7.9: Linear Dynamical Systems
- 7.10: An Application to Correlation and Variance
- 7.11: An Application to Differential Equations
- 8: Appendices
- This page presents fundamental mathematical concepts such as complex numbers, proofs, mathematical induction, and polynomials. It discusses the properties and applications of complex numbers, various forms of proof methods, and the use of mathematical induction for proving statements about infinite cases. Additionally, the structure and importance of polynomials are explored, with selected exercise answers to reinforce the material.
- 8.A: Complex Numbers
- 8.B: Proofs
- 8.C: Mathematical Induction
- 8.D: Polynomials
- 8.E: LU-Factorization
- 8.F: Proof of the Cofactor Expansion Theorem
- 8.G: Isomorphisms and Composition
- 8.H: A Theorem about Differential Equations
- 8.I: More on Linear Recurrences
- 8.J: Orthogonality
- 8.K: Change of Basis
- 8.L: Canonical Forms
- 8.M: Inner Product Spaces


