Sample Midterm 1, Sample Midterm 2 (for 4b, we say matrices are similar if they have the same eigen-values). I won’t post solutions but you can ask about solutions in office hours.

Closed book, no notes, not calculators

Covers everything through the notes on Change of Basis

A proportion of the test will cover the more straightforward algorithmic problems. For this proportion of the test, be sure you understand/can do the following:

Compute determinant

Gaussian elimination

Matrix diagonalization

Find a basis for R(A) (using columns of A), and a basis for N(A)

Solve linear recurrence relations (e.g., Fibonacci numbers)

(MLC for short) A space for undergraduate students to study math together, with friendly support from tutors, who are graduate and undergraduate students in the math department. The MLC is located at LSK301 and LSK302. Every undergraduate student studying Math is welcome there! In the MLC, students may join the study groups if students wish to. Please note that while students are encouraged to seek help with homework, the MLC is not a place to check answers or receive solutions, rather, the aim is to aid students in becoming better learners and to develop critical thinking in a mathematical setting. For additional information please visit the website.

Notes. Written by Richard Anstee. These give an intuitive approach to our material.

Vector space, field, axioms. (Abstract definition of vector space. Missing one axiom: For every v ∈ V, there exists an element −v ∈ V, called the additive inverse of v, such that v + (−v) = 0)

Reference for singular value decomposition (svd): Linear Algebra and its Applications, by D. Lay, Editions 4 or 5 (but not 3). You can find it at the library. Here is a pdf of the relevant chapter: SVD.

Assignment 3. Due Friday, October 4, at the beginning of class. Solutions. Note: You have two weeks for this one, but it’s not twice as long! For students who are rewriting parts of the first assignment, please take advantage. Also, please practice Gaussian elimination until you are comfortable with it. (See Linear Algebra and its Applications, by D. Lay, for plenty of examples.)

## Linear Algebra (Honours)

Instructor:Yaniv PlanOffice: 1219 Math Annex

Email: yaniv (at) math (dot) ubc (dot) ca

Lectures:MWF 10:00-11:00am, MATX 1100.Office hours:Course outline, grading scheme, etc.:Outline.Midterm:Wednesday, October 23, in class.Change of BasisMath Learning Centre.(MLC for short) A space for undergraduate students to study math together, with friendly support from tutors, who are graduate and undergraduate students in the math department. The MLC is located at LSK301 and LSK302. Every undergraduate student studying Math is welcome there! In the MLC, students may join the study groups if students wish to. Please note that while students are encouraged to seek help with homework, the MLC is not a place to check answers or receive solutions, rather, the aim is to aid students in becoming better learners and to develop critical thinking in a mathematical setting. For additional information please visit the website.

Notes.Written by Richard Anstee. These give an intuitive approach to our material.2×2 matrices warm up. Matrix algebra.

2×2 matrices determinants and inverses.

Putnam problem about integer coefficients.

2×2 matrices are linear transformations are 2×2 matrices.

Different levels of generality. (Not discussed in class. Read me!)

Eigenvectors and eigenvalues (long run behaviour of bird populations).

Fibonacci numbers.

Change of coordinates.

Gaussian elimination.

Determinants.

Interchanging rows changes sign of determinant. (Technical part of proof from “Determinants”. Not discussed in class.)

Partial Fractions.

Vector space, field, axioms. (Abstract definition of vector space. Missing one axiom: For every

v∈V, there exists an element −v∈V, called the additive inverse ofv, such thatv+ (−v) =0)Intuitive intro to abstract vector space. Subspace. Span.

Linear independence, spanning set, dimension.

Row space, column space, and rank.

Change of basis.

Systems of differential equations.

Complex numbers 1 and Complex numbers 2.

Vector geometry. Dot product, length, angle in high dimensions.

Orthogonal vector spaces.

Gram Schmidt algorithm, orthonormal basis, orthonormal matrix.

Orthogonal projections and least squares.

Symmetric matrices are orthogonally diagonalizable.

Reference for singular value decomposition (svd): Linear Algebra and its Applications, by D. Lay, Editions 4 or 5 (but not 3). You can find it at the library. Here is a pdf of the relevant chapter: SVD.

Homework.Assignment 1. Due Friday, September 13, at beginning of class. Solutions.

Assignment 2. Due Friday, September 20, at beginning of class. Solutions.

Assignment 3. Due Friday, October 4, at the beginning of class. Solutions.

Note:You have two weeks for this one, but it’s not twice as long! For students who are rewriting parts of the first assignment, please take advantage. Also, please practice Gaussian elimination until you are comfortable with it. (See Linear Algebra and its Applications, by D. Lay, for plenty of examples.)Assignment 4. Due Friday, October 11, at the beginning of class. Solutions.

Assignment 5. Due Friday, October 18, at the beginning of class. Solutions.