Time: Tuesday, Friday, 12:00 to 1:50 PM
Room: Darrin 337

Instructor: Gregor Kovacic
Office: 420 Amos Eaton
Phone: 276-6908
E-mail: kovacg at rpi dot edu
Office Hours: Click here

Teaching Assistant: Sijia Huang
Office: 424 Amos Eaton
E-mail: huangs6 at rpi dot edu
Office Hours: Monday, Wednesday, Thursday, 12:30 to 1:30 PM

Teaching Assistant: Nour Al Hassaineh
Office: 433 Amos Eaton
E-mail: alhasn at rpi dot edu
Office Hours: Monday, Wednesday, Thursday, 10:00 to 11:00 AM

  • This document contains the list of topics to be presented in this course, a list of textbooks that will be on reserve in the library, and some comments and recommendations about these textbooks.
  • Click on each topic title to download the notes for that topic.
  • To check out the grading and homework rules, click here.
  • To see the test dates and topics, click here.
  • To find a discussion of the homework assignments, click here.
  • To find statements of attendance policy, academic integrity, how to compute your current grade, grade appeals, etc., click here.


    Click on each topic title to download the notes for that topic. You can access much nicer notes below.

    Vectors in the three-dimensional space: Vectors and scalars, vector sum, multiplication by a scalar, dot product, cross product, triple product, line and plane in space, distance between points and lines and planes, sphere and cone. Orthogonal and non-orthogonal coordinate systems, basis.

    Complex numbers: Sum, product, absoulte value, representation in the plane, complex conjugate, absolute value, polar representation, De Moivre's formula, roots of unity, triangle inequality, complex exponential.

    Systems of linear equations: Gausian elimination, overdetermined and underdetermined systems.

    Vector spaces: Vectors and scalars, linear combinations, linear independence, basis, linear subspaces, direct sums.

    Euclidean and unitary spaces: Inner product, Cauchy-Schwartz and triangle inequalites, distance in n-dimensional spaces, angle between two vectors, orthogonality, orthogonal bases, Gram-Schmidt orthogonalization, orthogonal complements.

    Linear operators and matrices: Matrix of a linear operator in a given basis, algebra of linear operators, change of basis, adjoint operators, range and kernel, rank and nullity, Fredholm alternative, linear functionals, self-adjoint, orthogonal, unitary, and positive definite operators.

    Determinants: Projections and normals, volumes and oriented volumes in n-dimensional spaces, algebraic properties of determinants, minors and cofactors, multiplication of determinats, calculation of inverse matrices, Cramer's rule.

    Eigenvalues and eigenvectors: Characteristic polynomial, spectrum, diagonalization, spectral theory of normal, self-adjoint, and unitary operators, simultaneous diagonalization and commutativity, positive definite matrices and polar decomposition.

    Bilinear and quadratic forms: Reduction of a quadratic form to a sum of squares, law of inertia, diagonalizing a quadratic form using elementary row and column operations, simultaneous diagonalization of matrices and quadratic forms.

    Singular-value decomposition: Incompatible systems and the method of least squares, normal system, singular value decomposition, low-rank approxmation of a matrix, quasi-inverse.

    Jordan normal form of a matrix: Alegbraic and geometric multiplicity of an eigenvalue, matrices that cannot be diagonalized, invariant subspaces and direct sums of operators, nilpotent matrices, Jordan normal form, minimal polynomial, Cayley-Hamilton theorem.

    The daily build of class notes is deposited here.
    Class notes from the Summer session of 2018 are deposited here.
    Class notes from the Fall semester of 2017 are deposited here.
    Class notes from the Fall semester of 2014 are deposited here.
    Class notes from the Fall semester of 2012 are deposited here.


    The following textbooks contain material similar to that presented in this course:

    T. M. Apostol, Calculus, Vol. 2: Multi-Variable Calculus and Linear Algebra with Applications to Differential Equations and Probability, Wiley.
    M. A. Akivis and V. V. Goldberg, An Introduction to Linear Algebra and Tensors, Dover.
    R. Courant and F. John, Introduction to Calculus and Analysis, Vol. II/1, Springer.
    C. G. Cullen, Matrices and Linear Transformations, Dover.
    H. Dym, Linear Algebra in Action, AMS.
    J. N. Franklin, Matrix Theory, Dover.
    F. R. Gantmacher, The Theory of Matrices, Chelsea.
    I. M. Gelfand, Lectures on Linear Algebra, Dover.
    P. R. Halmos, Finite-Dimensional Vector Spaces, Springer Verlag.
    L. Mirsky, An Introduction to Linear Algebra, Dover.
    P. D. Lax, Linear Algebra and its Applications, Wiley.
    S. Lipschutz, Schaum's Outline of Theory and Problems of Linear Algebra, Schaum's Outline Series, McGraw-Hill.
    M. Marcus and H. Minc, Introduction to Linear Algebra, Dover.
    J. T. Scheick, Linear Algebra with Applications, McGraw-Hill.
    G. E. Shilov, Linear Algebra, Dover.
    G. Strang, Introduction to Linear Algebra, Wellesley Cambridge Press.
    G. Strang, Linear Algebra and Its Applications, Brooks Cole.

    Comments and recommendations about textbooks

    Do not buy any books until after you have attended the first lecture. I will not follow any single texbook in class, and therefore, there is no prescribed class textbook. At this level, you should be able to look at a few books, and figure out which of them suit(s) your style. If you really like one (or a few), buy it. If not, the class notes and homework will suffice. I will here comment on a few books that I know well and like. This does not mean that they are the best books on linear algebra, or that there are no other good books on the subject. If you want to read a book with a really nice, somewhat more matrix-oriented perspective than the course, read one of the books by Strang. They are very popular texbooks, written by a great expert in computational mathematics from a practical standpoint. He really explains the material well without any unnecessary abstraction. The books by Gelfand and Shilov are old classics from the Russian school. I have ordered three of these for the bookstore, because they are well written, not too abstract, and cheap. They won't cover everything that I will teach in the class. You don't need to buy them, but if you want a book to look at, any of these is just fine. The book by Halmos is a U.S. classic, probably the first modern exposition of linear algebra. They are on a somewhat high level, especially the book by Halmos. The book by Lipschutz is a good reference and source of practical problems. The books by Akivis and Goldberg, Cullen, Franklin, Marcus and Minc, Mirsky, and Scheick are nice textbooks. The books by Dym and Lax are contemporary classics, rather advanced, and written at a very high level. The two books by Gantmacher are old monographs on matrix theory, full of results that cannot be found elsewhere. The books by Apostol and Courant and John are high-level calculus/introductory mathematical analysis books with good chapters on some aspects of linear algebra. I will use material from many of these books in my lectures. Finally, here is a nice book in electronic form.

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