Math E-21b: Linear Algebra - Spring 2019

**Meeting Times**: Class will meet in **a Harvard Hall 201** every Thursday from 8:00pm to 10:00pm starting January 31. Optional weekly problem sessions will be scheduled based on the preferences of the class and the Teaching Assistants and will begin during the second week of the course. There will also be an optional Q&A session before class each week in Harvard Hall 103. Other times may be available on request for questions.

**Instructor**: Robert Winters, Lecturer of Mathematics, Mass. Institute of Technology (MIT), Concourse Program. Contact me at .

**Course website**: **http://math.rwinters.com/E21b** (all assignments and solutions will be posted here) and **Canvas site**

**Teaching Assistants**: Jeremy Marcq & Renée Chipman

**Prerequisites**: Math E-16, or equivalent knowledge of algebra and calculus. You should be able to solve simple systems of equations and find the roots of polynomials. Also, you should be able to set up and solve simple differential equations. Math E-21a (or its equivalent) is not specifically necessary in order to take Math E-21b, but it will be very helpful if you have some familiarity with the algebra and geometry of lines and planes in **R**^{2}, **R**^{3}, and possibly **R**^{n}, and the dot product of two vectors.

**Note**: The "Graduate" credit option is primarily for students enrolled in certain Extension School graduate programs such as the "Math for Teaching" program. All other students (including high school students) should register for the "Undergraduate" option or the Noncredit option (if you will not be submitting homework or taking exams).

**Philosophy**: This course is greatly dependent upon your participation. Most of the the mathematical concepts and techniques will be presented in class, with plenty of opportunity for questions and clarification, but the best lessons learned are those derived from discussion and practice. Outside of class, it is essential that you read the assigned text sections, do the assigned homework, and bring any questions to class or to the course assistant's section. __Mathematics is not a spectator sport__. Don’t just crank through computations – think about the problems posed, your strategy, the meaning of the computations you perform, and the answers you get. This will be the best preparation for interaction in the classroom and for the exams.

**Homework**: Problem sets will be posted each week in the **Calendar** section of the course website, including a PDF version for those using other texts. Each assignment will be due at the following class or online a few days later submitted as a scanned PDF via the Canvas site. Graded paper submissions will be returned the following class. All policies regarding late homework will rest with the Teaching Assistants who will be reading and grading the assignments. All submitted homework must be neat, with answers boxed when appropriate. Multiple pages __must__ be stapled together and any homework submitted online must be in a single PDF file. Solutions will be posted on the course website as PDF documents.

You are encouraged to discuss the homework with your fellow students, but __you must write up the solutions by yourself without collaboration with others__. (This is simply a matter of professional ethics.) Any unethical behavior on the homework (*such as copying solutions from a solutions manual*) may result in significant penalties or only exam scores being used in the calculation of your course grade.

Please note that **the reading assigned with each homework is essential**. Some topics not covered fully in class will be left to the reading and you will be expected to pick up those additional details. Questions on the homework and the reading may also be directed to me at .

Some of the homework problems will look different than problems discussed in class and in the text. This is not an accident. We want you to *think* about the material and learn to apply it in unfamiliar settings and interpret it in different ways. Only if you understand the material (as opposed to merely knowing it) will you be able to go beyond the information you are given.

**Exams and Grading**: Two in-class midterm exams are currently scheduled for the last half of class on __March 7__ and __April 25__. There will be a two-hour final exam on __May 16__. Your course grade will be computed according to the following scheme, subject to minor modification:

.20(hour exam 1) + .20(hour exam 2) + .20(homework) + .40(final exam)

**Text**: ** Linear Algebra With Applications, 4th Edition** (2008) or

**Use of Technology**: In some of the homework problems you will be asked not to use any technology (calculators or software packages). If no restriction is made, you may use the form of technology of your choice, e.g. TI calculators, Matlab, Maple, Mathematica. Make sure to have access to some form of technology. Calculators (as opposed to computers) will be permitted on exams, and it will be helpful if you are familiar with the matrix operations on a hand-held calculator, especially finding the reduced row-echelon form of a matrix. An effort will be made to write the exams in such a way that all problems may be solved without technology.

Mathematics E-21b Topics

(This plan is ambitious and may have to be trimmed. Some topics may be omitted.)

Date (approximate) |
Text sections |
Topics |

Thurs, Jan 31 | 1.1: Introduction to Linear Systems 1.2: Matrices, Vectors, and Gauss-Jordan Elimination 1.3: On the Solutions of Linear Systems; Matrix Algebra |
Algebra and geometry of lines, planes; solving equations simultaneously; row reduction and row operations; rank of a matrix; homogeneous vs. inhomogeneous systems. |

Thurs, Feb 7 | 2.1: Introduction to Linear Transformations and their Inverses 2.2: Linear Transformations in Geometry 2.3: Matrix Products |
Linear transformations from R^{m} to R^{n}; linearity; domain and codomain; invertibility; meaning of the columns of a matrix; rotations and dilations; shears; projections and reflections. Matrix algebra, associativity and the composition of linear functions. |

Thurs, Feb 14 | 2.4: The Inverse of a Linear Transformation 3.1: Image and Kernel of a Linear Transformation 3.2: Subspaces of R^{n}; Bases and Linear Independence |
Inverse of a matrix; image and kernel of a linear transformation; linear combinations and the span of a set of vectors; subspaces; linear independence; basis. |

Thurs, Feb 21 | 3.3: The Dimension of a Subspace of R^{n}3.4: Coordinates |
Dimension of a subspace; bases for kernels and images; Rank-nullity Theorem; coordinates of a vector relative to a basis; matrix of a linear transformation relative to a (nonstandard) basis. |

Thurs, Feb 28 | 4.1: Introduction to Linear Spaces 4.2: Linear Transformations and Isomorphisms 4.3: The Matrix of a Linear Transformation |
Examples of linear spaces other than R^{n}, e.g. function spaces. Linear spaces; isomorphisms; coordinates; matrix of a general linear transformation relative to a basis. |

Thurs, Mar 7 | 5.1: Orthogonal Projections and Orthogonal Bases 5.2: Gram-Schmidt Process and QR Factorization (last half of class)Midterm Exam 1 |
Orthogonality (perpendicularity) of vectors in R^{n}; length (norm) of a vector, unit vectors; orthogonal complements; orthogonal projections; orthonormal basis; angle between two vectors; Gram-Schmidt orthogonalization process; QR factorization. |

Thurs, Mar 14 | 5.2: Gram-Schmidt Process and QR Factorization 5.3: Orthogonal Transformations and Orthogonal Matrices 5.4: Least Squares and Data Fitting |
Gram-Schmidt orthogonalization process; QR factorization; orthogonal transformation; orthogonal matrix. Least-squares approximation; normal equation. |

Thurs, Mar 28 | 6.1: Introduction to Determinants 6.2: Properties of the Determinant 6.3: Geometrical Interpretations of the Determinant; Cramer's Rule |
Determinant of a (square) matrix; multilinearity; minors, cofactors, and adjoints; k-volumes; determinant as an expansion factor; Cramer's Rule. |

Thurs, Apr 4 | 7.1: Dynamical Systems and Eigenvectors: An Introductory Example 7.2: Finding the Eigenvalues of a Matrix 7.3: Finding the Eigenvectors of a Matrix |
Discrete (linear) dynamical system; iteration of a matrix; eigenvectors and eigenvalues of a (square) matrix; characteristic polynomial; algebraic and geometric multiplicities. |

Thurs, Apr 11 | 7.4: Diagonalization 7.5: Complex Eigenvalues |
Similarity of matrices; diagonalization and the existence of a basis of eigenvectors; powers of a matrix; eigenvalues of a linear transformation. Complex numbers; De Moivre's formula; rotation-dilation matrices revisited; trace and determinant. |

Thurs, Apr 18 | 7.5: Complex Eigenvalues 7.6: Stability |
Complex eigenvalues, repeated eigenvalues. Stability of a discrete linear dynamical system; phase portraits. |

Thurs, Apr 25 | 8.1: Symmetric matrices 8.2: Quadratic Forms (last half of class)Midterm Exam 2 |
Spectral Theorem; symmetric matrices and diagonalization by an orthonormal basis; quadratic forms; positive definiteness of a matrix; principal axes; applications to ellipses and hyperbolas; 2nd derivative test for functions of several variables in terms of eigenvalues. |

Thurs, May 2 | 9.1: An Introduction to Continuous Dynamical Systems 9.2: The Complex Case: Euler’s Formula 9.3: Linear Differential Operators and Linear Differential Equations |
Systems of linear differential equations and their solutions. Eigenfunctions, characteristic polynomials; kernel and image of a linear differential operator; solutions to homogeneous and inhomogeneous linear differential equations. |

Thurs, May 9 | 9.3: Linear Differential Operators Nonlinear Systems of Differential Equations |
Further topics in differential equations. |

Thurs, May 16 | FINAL EXAM |
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