2240 class highlights

  • Tues May 3
    Share the final research presentations topic (name, major(s), concentrations/minors, research project idea, and whether you prefer to go 1st, 2nd or have no preference).
    Reflection
    final research presentations
    sample project,
    full guidelines
    formal evaluations

  • Thur Apr 28 Test 3 corrections, study guide
    Applications to mathematical physics, quantum chemistry...
    Eigenfunction
    Tacoma Narrows
    final research presentations,
    sample project,
    full guidelines
    Informal evaluations.

  • Tues Apr 26 Test 3
  • Thur Apr 21 review for test 3 and take questions on the study guide or final research presentations
    Clicker question on interests
    April 2014 was mathematics awareness month on magic and mystery.
    Making a matrix disappear and then reappear Look at
    h,P:=Eigenvectors(A)
    MatrixInverse(P).A.P
    which (ta da) has the eigenvalues on the diagonal (when the columns of P form a basis for Rn)- diagonalizability. [We can uncover the mystery and apply this to computer graphics].

  • Tues Apr 19
    Clicker questions---review of eigenvectors
    April is Mathematics Awareness Month on The Future of Prediction
    final research presentations Chinese, German Gauss, French Laplace, German polymath Hermann Grassman (1809-1877) 1844: The Theory of Linear Extension, a New Branch of Mathematics (extensive magnitudes---effectively linear space via linear combinations, independence, span, dimension, projections.)
    sample project,
    full guidelines
    Big picture discussion
  • Thur Apr 14
    Clicker questions--- eigenvector decomposition (5.6) part 2 #3-6
    Fill in examples on Terms for Test 3
    Dynamical Systems and Eigenvectors remaining examples
    final research presentations
    Hamburger earmuffs and the pickle matrix
    sample project,
    full guidelines
    THE $25,000,000,000 EIGENVECTOR by Kurt Bryan and Tanya Leise: When Google went online in the late 1990's, one thing that set it apart from other search engines was that its search result listings always seemed deliver the "good stuff" up front. With other search engines you often had to wade through screen after screen of links to irrelevant web pages that just happened to match the search text. Part of the magic behind Google is its PageRank algorithm, which quantitatively rates the importance of each page on the web, allowing Google to rank the pages and thereby present to the user the more important (and typically most relevant and helpful) pages first.
    About once a month, Google finds an eigenvector of a matrix that represents the connectivity of the web (of size billions-by-billions) for its pagerank algorithm.
    http://languagelog.ldc.upenn.edu/nll/?p=3030

  • Tues Apr 12
    Clicker questions on eigenvector decomposition (5.6) part 1#3-4 [Solutions: 1. a), 2. c), 3. c), 4. b)]
    Review reflection across y=x line via pictures. A few inputs. Where is the output? Is the vector an eigenvector?
    >Ex1:=Matrix([[0,1],[1,0]]);
    >Eigenvalues(Ex1);
    >Eigenvectors(Ex1);
    Geometry of Eigenvectors examples 1 and 2 and compare with Maple
    >Ex2:=Matrix([[0,1],[-1,0]]);
    >Ex3:=Matrix([[-1,0],[0,-1]]);
    >Ex4:=Matrix([[1/2,1/2],[1/2,1/2]]);
    Horizontal shear Matrix([[1,k],[0,1]]) and via det (A-lamda I)=0. Once given lambda, what is the eigenvector?
    Clicker questions--- eigenvector decomposition (5.6) part 2 #1 and 2
  • Thur Apr 7 Review Eigenvalues and Eigenvectors and the Eigenvector decomposition
    Clicker questions in 5.1#1-3
    eigensheep comic
    M := Matrix([[6/10,4/10],[-125/1000,12/10]]);
    Eigenvectors(M);
    Application: Foxes and Rabbits
    Also revisit the black hole matrix.
    Why we use the eigenvector decomposition versus high powers of A for longterm behavior (reliability)
    Compare with Dynamical Systems and Eigenvectors first example
    Clicker questions on eigenvector decomposition (5.6) part 1#1-2
    Highlight predator prey, predator predator or cooperative systems (where cooperation leads to sustainability)
    Eigenvector comic 2

  • Tues Apr 5
    basis, null space and column space
    Clicker questions in 2.8
    Applications.
    Eigenvalues and applications (2.8, 5.1, 5.2 and 5.6) (after test 3: chap 7 selections)
    Begin 5.1: the algebra of eigenvectors and eigenvalues, and connect to geometry and Maple.
    Eigenvalues and eigenvectors via the algebra as well as the geometry.
    Eigenvalues or triangular matrices like shear matrix are on the diagonal-- characteristic equation.
    Matrix([[2,1],[1,2]])
    M := Matrix([[2,1],[1,2]]);
    Eigenvectors(M);
    Eigenvector comic 1
    Begin 5.6: Eigenvector decomposition for a diagonalizable matrix A_nxn [where the eigenvectors form a basis for all of Rn].
  • Thur Mar 31 Review determinants.
    Clicker questions in Chapter 3 9
    Catalog description: A study of vectors, matrices and linear transformations, principally in two and three dimensions, including treatments of systems of linear equations, determinants, and eigenvalues.
    If space is the final frontier, then what's a subspace? subspace, basis, null space and column space
    2.8 using the matrix 123,456,789 and finding the Nullspace and ColumnSpace (using 2 methods - reducing the spanning equation with a vector of b1...bn, and separately by examining the pivots of the ORIGINAL matrix.) Add to the terms. Two other examples.
    nullspace
  • Thur Mar 24
    Test 2 corrections
    LaTeX Beamer Slides
    glossary of terms
    Clicker questions in Chapter 3 #10 Questions on 3.1 or 3.2.
    The determinant of A inverse via the determinant of the product of A and A inverse - and via elementary row operations - so det A non-zero can be added into Theorem 8 in Chapter 2: What Makes a Matrix Invertible.
    Mention google searches: application of determinants in physics application of determinants in economics application of determinants in chemistry application of determinants in computer science Eight queens and determinants application of determinants in geology: volumetric strain
    Clicker questions in Chapter 3 4-8
    3.3 p. 180-181:
    The relationship of row operations to the geometry of determinants - row operations can be seen as vertical shear matrices when written as elementary matrix form, which preserve area, volume, etc.

  • Tues Mar 22 Test 2. Hand out glossary of terms
  • Thur Mar 17
    Clicker questions in Chapter 3 #1-3
    Chapter 3 in Maple via MatrixInverse command for 2x2 and 3x3 matrices and then determinant work, including 2x2 and 3x3 diagonals methods, and Laplace's expansion (1772 - expanding on Vandermonde's method) method in general. [general history dates to Chinese and Leibniz]
    M:=Matrix([[a,b,c],[d,e,f],[g,h,i]]);
    Determinant(M); MatrixInverse(M);
    M:=Matrix([[a,b,c,d],[e,f,g,h],[i,j,k,l],[m,n,o,p]]);
    Determinant(M); MatrixInverse(M);
    LaTex Beamer slides
    Review the diagonal determinant methods for the 123,456,789 matrix and introduce the Laplace expansion. Review that for 4x4 matrix in Maple, only Laplace's method will work.
    The determinator comic, which has lots of 0s
    The connection of row operations to determinants
    The determinant of A transpose and A triangular (such as in Gaussian form).

    Overview of new material for test 2 and take questions.

  • Tues Mar 15
    review linear transformations
    Clicker questions in 2.7 #3-6
    Keeping a car on a racetrack
    Clicker questions in 2.7 #7
    Review linear transformations of 3-space: Computer graphics demo [2.7] Examples 3-5
    Begin Yoda (via the file yoda2.mw) with data from Kecskemeti B. Zoltan (Lucasfilm LTD) as on Tim's page
    Clicker questions in 2.7 #8
  • Thur Mar 3
    Clicker questions in 2.7 #1.
    general geometric transformations on R2 [1.8, 1.9]
    In the process, review the unit circle
    Computer graphics demo [2.7]
    review linear transformations
    Clicker questions in 2.7 #2

  • Tues Mar 1
    Go over 2.3 #11c and 12e on solutions
    Review Hill Cipher and Condition Number
    Clicker questions in 2.3 and Hill Cipher and Condition Number #3-4
    Applications of 2.1-2.3: 1.8 (p. 62, 65, & 67-68), 1.9 (p. 70-75), and 2.7
    Guess the transformation. In the process, discuss that the first column of the matrix representation is the same as the output of the unit x vector, and that invertible matrices will take the plane to the plane (the range is onto the plane), while matrices that are not invertible do not span the entire plane, so they smush the plane (pictures in the plane, etc).
    Mirror mirror comic and Sheared Sheap comic
    general geometric transformations on R2 [1.8, 1.9]
    In the process, review the unit circle
  • Thur Feb 25
    Clicker questions in 2.3 and Hill Cipher #1-2
    2.2 #21 problematic reasoning: If the 2 columns of A are multiples the determinant will be 0. OR incomplete: the columns of A are li because Ax=0 has only the trivial solution when A is invertible.
    Review What Makes a Matrix Invertible
    Comic: associativity superpowers
    Review linear transformations: Ax=b where A is fixed, x are given like in a code or the plane and we see or use the b outputs.
    Applications of 2.1-2.3: Linear transformations in the cipher setting and finish 2.3 via the condition number.
    Hill Cipher history
    Maple file on Hill Cipher and Condition Number and PDF version
    review of Hill cipher and condition number

  • Tues Feb 23
    Glossary of Terms
    Clicker questions in 2.2 #1-3
    Review the Applications of multiplication and the inverse (if it exists)
    Review 2.2 Algebra: Inverse of a matrix
    In groups of 2-3 people, assume that A (square) has an inverse. What else can you say?

    Theorem 8 in 2.3 [without linear transformations]: A matrix has a unique inverse, if it exists. A matrix with an inverse has Ax=b with unique solution x=A^(-1)b, and then the columns span and are l.i...
    What makes a matrix invertible
    Discuss what it means for a square matrix that violates one of the statements. Discuss what it means for a matrix that is not square (all bets are off) via counterexamples.
    -2.1-2.3 Applications: Hill Cipher, Condition Number and Linear Transformations (2.3, 1.8, 1.9 and 2.7)
    Applications: Introduction to Linear Maps
    The black hole matrix: maps R^2 into the plane but not onto (the range is the 0 vector).
    Dilation by 2 matrix

    Linear transformations in the cipher setting:
    A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
    1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26
  • Thur Feb 18
    Review 2.1 #21.
    multiply comic
    2.2: Multiplicative Inverse for 2x2 matrix:
    twobytwo := Matrix([[a, b], [c, d]]);
    MatrixInverse(twobytwo);
    MatrixInverse(twobytwo).twobytwo
    simplify(%)
    2.2 Algebra: Inverse of a matrix.
    Repeated methodology: multiply by the inverse on both sides, reorder by associativity, cancel A by its inverse, then reduce by the identity to simplify:
    Applications of multiplication and the inverse (if it exists).
    Clicker in 2.1 and 2.2 continued: #7-8.
    Test 1 corrections
  • Tues Feb 16 Test 1
  • Thur Feb 11
    Continue matrix algebra via Clicker questions in 2.1 5 and 6 in LaTeX.
    matrix multiplication and matrix algebra. AB not BA...
    Introduce transpose of a matrix via Wikipedia, including Arthur Cayley. Applications including least squares estimates, such as in linear regression, data given as rows (like Yoda).
    Clicker review questions 6-7
    Test 1 review part 2
    Take review questions for test 1.

  • Tues Feb 9
    Test 1 review part 1
    Begin Chapter 2:
    via Clicker questions in 2.1 1-4
    Image 1   Image 2   Image 3   Image 4   Image 5   Image 6   Image 7.
    Clicker review questions 6-7
  • Thur Feb 4 Review
    1.7 definition of linearly independent
    dependence comic
    Maple commands
    Roll Yaw Pitch Gimbal lock on Apollo 11.
    clicker review questions 1-5

  • Tues Feb 2 Clicker question in 1.3 and 1.5 #5
    Practicing Linear Algebra and ASULearn grades
    Clicker question to motivate 1.7
    How to express redundancy?
    1.7 definition of linearly independent and connection to efficiency of span
    In R^2: spans R^2 but not li, li but does not span R^2, li plus spans R^2.
    Clicker questions in 1.7 and the theorem about l.i. equivalences in 1.7
  • Thur Jan 28
    Theorem 4 in 1.4
    Clicker question in 1.4
    Coff:=Matrix([[.3,.4,36],[.2,.3,26],[.2,.2,20],[.3,.1,18]]);
    ReducedRowEchelonForm(Coff);
    Coffraction:=Matrix([[3/10,4/10,36],[2/10,3/10,26],[2/10,2/10,20],[3/10,1/10,18]]);
    ReducedRowEchelonForm(Coffraction);
    Decimals (don't use in Maple) and fractions. Geometry of the columns as a plane in R^4, of the rows as 4 lines in R^2 intersecting in the point (40,60).
    1.5: vector parametrization equations of homogeneous and non-homogeneous equations. Introduce t*vector1 + vector2 is the collection of vectors that end on the line parallel to vector 1 and through the tip of vector 2

  • Tues Jan 26 Review vectors, addition, scalar multiplication, linear combinations and span of them
    What's your span? comic.
    Clicker questions in 1.3 and 1.5 # 3-4
    discuss what happens when we correctly use GaussianElimination(s13n15extension) - write out the equation of the plane that the vectors span.
    s13n15extension:=Matrix([[1,-5,b1],[3,-8,b2],[-1,2,b3]]);
    GaussianElimination(s13n15extension);
    Choose a vector that violates this equation to span all of R^3 instead of the plane and plot:
    M:=Matrix([[1,-5,0,b1],[3,-8,0,b2],[-1,2,1,b3]]);
    GaussianElimination(M);
    a:=spacecurve({[t, 3*t, -1*t, t = 0 .. 1]}, color = red, thickness = 2):
    b:=spacecurve({[-5*t, -8*t, 2*t, t = 0 .. 1]}, color = blue, thickness = 2):
    diagonalparallelogram:=spacecurve({[-4*t, -5*t, -1*t, t = 0 .. 1]}, color = black, thickness = 2):
    c:=spacecurve({[0, 0, t, t = 0 .. 1]}, color = magenta, thickness = 2):
    display(a,b,c,diagonalparallelogram);
    Begin 1.4. Ax via using weights from x for columns of A versus Ax via dot products of rows of A with x and Ax=b the same (using definition 1 of linear combinations of the columns) as the augmented matrix [A |b]. The matrix vector equation and the augmented matrix. The matrix vector equation and the augmented matrix and the connection of mixing to span and linear combinations.
    Theorem 4 in 1.4
  • Thur Jan 21 Collect problem set 1. Hand out Glossary 2: More Terms for Test 1
    History of linear equations and the term "linear algebra" images, including the Babylonians 2x2 linear equations, the Chinese 3x3 column elimination method over 2000 years ago, Gauss' general method arising from geodesy and least squares methods for celestial computations, and Wilhelm Jordan's contributions.
    Gauss quotation. Gauss was also involved in other linear algebra, including the history of vectors, another important "linear" object.
    vectors, scalar mult and addition, Foxtrot vector addition comic by Bill Amend. November 14, 1999.
    1.3 linear combinations and weights, vector equations and connection to 1.1 and 1.2 systems of equations and augmented matrix. linear combination language (addition and scalar multiplication of vectors).
    c1*vector1 + c2*vector2_on_a_different_line is a plane via:
    span1:=Matrix([[1, 4, b1], [2, 5, b2], [3, 6, b3]]);
    GaussianElimination(span1);
    Comment on the span being b1-2b2+b3=0. Notice that Vector([7,8,9]) also satisfies this equation, and we can turn the plane they are in "head on" in Maple in order to see that no 2 lie on the same line but all are in the same plane:
    a1:=spacecurve({[t, 2*t, 3*t, t = 0 .. 1]}, color = red, thickness = 2):
    a2:=textplot3d([1, 2, 3, ` vector [1,2,3]`], color = black):
    b1:=spacecurve({[4*t,5*t,6*t,t = 0 .. 1]}, color = green, thickness = 2):
    b2:=textplot3d([4, 5, 6, ` vector [4,5,6]`], color = black):
    c1:=spacecurve({[7*t, 8*t, 9*t, t = 0 .. 1]},color=magenta,thickness = 2):
    c2:=textplot3d([7,8,9,`vector[7,8,9]`],color = black):
    display(a1,a2,b1,b2,c1,c2);
    Replace with [7, 8, 10] which is not in the span.
    Clicker questions in 1.3 and 1.5 # 1, 2

  • Tues Jan 19
    Collect hw. Go over the glossary on ASULearn, solutions, hints, and advice from the last run of the class.
    Review the algebra and geometry of eqs with 3 unknowns in R^3
    Clicker questions in 1.1 and 1.2 continued
  • Thur Jan 14 Turn in hw. Register the i-clickers.
    Gaussian and Gauss-Jordan for 3 equations and 2 unknowns in R2.
    Clicker on 3eqs 2 vars
    Clicker questions 1.1 and 1.2 #1.
    Mention solutions and a glossary on ASULearn.
    Prepare to share your major(s)/minors/concentrations as I call your name. Any questions?

    Gaussian and Gauss-Jordan or reduced row echelon form in general: section 1.2, focusing on algebraic and geometric perspectives and solving using by-hand elimination of systems of equations with 3 unknowns. Follow up with Maple commands and visualization: ReducedRowEchelon and GaussianElimination as well as implicitplot3d in Maple (like on the handout):
    Drawing the line comic. Solve the system x+y+z=1 and x+y+z=2 (0 solutions - 2 parallel planes)
    implicitplot3d({x+y+z=1, x+y+z=2}, x = -4 .. 4, y = -4 .. 4, z = - 4 .. 4);
    Parametrize x+y+z=1.
    with(plots): with(LinearAlgebra):
    Ex1:=Matrix([[1,-2,1,2],[1,1,-2,3],[-2,1,1,1]]);
    implicitplot3d({x-2*y+z=2, x+y-2*z=3, (-2)*x+y+z=1}, x = -4 .. 4, y = -4 .. 4, z = -4 .. 4);
    Ex2:=Matrix([[1,2,3,3],[2,-1,-4,1],[1,1,-1,0]]);
    implicitplot3d({x+2*y+3*z=3,2*x-y-4*z=1,x+y-z=0}, x=-4..4,y=-4..4,z=-4..4);
    Ex3:=Matrix([[1,2,3,0],[1,2,4,4],[2,4,7,4]]);
    implicitplot3d({x+2*y+3*z = 0, x+2*y+4*z = 4, 2*x+4*y+7*z = 4}, x = -13 .. -5, y = -1/4 .. 1/4, z = 3 .. 5, color = yellow);
    Ex4:=Matrix([[1,3,4,k],[2,8,9,0],[10,10,10,5],[5,5,5,5]]);
    GaussianElimination(Ex4);
    Ex4a:=Matrix([[1,3,4,k],[2,8,9,0],[10,10,10,5],[5,5,5,5]]);
    GaussianElimination(Ex4);
    Highlight equations with 3 unknowns with infinite solutions, one solution and no solutions in R3, and the corresponding geometry, as we review new terminology and glossary of terms.

  • Tues Jan 12 UTAustinXLinearAlgebra.mov. Manga comic
    Course intro slides # 1 and 2
    Work on the introduction to linear algebra handout motivated from Evelyn Boyd Granville's favorite problem (#1-3). At the same time, begin 1.1 (and some of the words in 1.2) including geometric perspectives, by-hand algebraic EBG#3, Gaussian Elimination and EBG #5 and pivots, solutions, plotting and geometry, parametrization and GaussianElimination in Maple for systems with 2 unknowns in R2.
    Evelyn Boyd Granville #3:
    with(LinearAlgebra): with(plots):
    implicitplot({x+y=17, 4*x+2*y=48},x=-10..10, y = 0..40);
    EBG3:=Matrix([[1,1,17],[4,2,48]]);
    GaussianElimination(EBG3);
    ReducedRowEchelonForm(EBG3);
    In addition, do #4
    Evelyn Boyd Granville #4: using the slope of the lines, versus full pivots in Gaussian (r2'=-4 r1 + r2):
    EBG4:=Matrix([[1,1,a],[4,2,b]]);
    GaussianElimination(EBG4);

    Course intro slides last 2 slides
    Evelyn Boyd Granville #5 with k as an unknown but constant coefficient.
    EBG#3, Gaussian Elimination and EBG #5
    EBG5:=Matrix([[1,k,0],[k,1,0]]);
    GaussianElimination(EBG5);
    ReducedRowEchelonForm(EBG5);
    Prove using geometry of lines that the number of solutions of a system with 2 equations and 2 unknowns is 0, 1 or infinite.

    How to get to the main calendar page: google Dr. Sarah / click on webpage / then 2240. Online HW
    Vocabulary/terms/ASULearn glossary