Welcome. This webpage contains many resources I have created for linear algebra. The current semester work is found in Blackboard, thanks!

- Course Planner test dates, homework assigned, etc...
- You Tube Playlist for Spring 2017 Linear Algebra I try to post each Lecture here soon after it is given
- You Tube Playlist from Linear Algebra of Spring 2016 (based on Curtis' text in part)
- Homework is due about every week, there will be about 130 total problems assigned. Each problem is worth 2pts, however, only certain problems will be graded generally. The wise course of action is to work ahead and ask questions in office hours. You can work together, but in the end your solution must be your own.
- I'm here to help. Please make wise use of my office hours when you get stuck. Also, in lecture, if I write something obviously wrong. Please politely interupt me before I burn 5 minutes of class on a bogus calculation. Do not wait until the end of class to tell me as we exit, you wrote linearly independent but meant linearly dependent. I have no patience for corrections! I want them immediately.
- Someone will be unable to make it to my office hours. It's inevitable in a given semester. Therefore, if you are such a person, it especially important for you to work with a study group which has at least one person who can make it to office hours.
- Homework will initially be posted in Blackboard. I don't remind you when it is due since the Course Planner (see above) has the due dates. Late work is not generally accepted.
- relax, it's just algebra!

- What am I asked to prove ?
- Can I define the words used ?

This course is similar in spirit to previous offerings, however, I am making several significant modifications in format.

- The websites linked below provide various matrix calculations online:
- Calculates the ref, rref, inverse and much more all while showing all the steps. very nice for problems without ugly decimals.
- Eigenvector calculator, ugly numbers no problem. Also deals with complex case no problem. However, does not find generalized e-vectors.
- Gram-Schmidt orthogonalizer: by Lawrence E. Turner of Southwestern Adventist University.
- Matrix Calculator Site - Calculates matrix inverse for you.
- Matrix Calculator Site - Multiplies matrices for you ( use to check answers )
- Wolfram Alpha: careful, may be addictive.
- Previous versions of my notes are found below. The fourth version of the 321 notes is what I expect you to read this semester along side the required text. I will post the notes as I write them throughout the semester (in Blackboard, I will announce when an update is made)
- Lecture Notes for Spring 2015
- linear algebra (2010 version) paired with Lay's text.
- linear algebra (2009 version) paired with Insel,Spence, Friedberg's basic text.
- Lecture Notes for Applied Linear Algebra (2012 version)

- The solutions below are from a previous semester.

- Homework 1: on linear systems and Gaussian Elimination
- Homework 2: matrix properties, elementary and inverse matrices
- Homework 3: calculating inverse matrix, and determinants, Kramer's Rule
- Homework 4: spanning sets, linear independence, CCP, coordinate vectors
- Homework 5: row, column and null space of a matrix
- Homework 6: linear transformations
- Homework 7: dot-products, Gram Schmidt, orthogonal complements
- Homework 8: orthogonal operators and least square fitting
- Homework 9: inner products, real e-values and e-vectors
- Homework 10: e-value theory and complex e-values and vectors
- Homework 11: diagonalization and eigenbases
- Homework 12: solving systems of ODEqns via the matrix exponential/e-vector method

- An interesting example: the mapping T(A)=BAB has rank 4 despite the fact B has rank 2. See this row reduction which is in response to Part 2 of Lecture 20 of Linear Algebra 2015.

The content of 2016 and 2015 is more or less identical, however, I follow a somewhat different path. In particular, I have structured the quizzes to cover material in series rather than in parallel with the major tests. In any event, there is still value in last year's Lectures, but, I do hope this year is an improvement.

- Lecture 1 part 1: sets, index notation, rows and columns
- Lecture 1 part 2: equality by components, rows or columns
- Lecture 2 part 1: functions, Gaussian elimination
- Lecture 2 part 2: row reduction for solving linear systems
- Lecture 3: solution sets, some theoretical results about rref
- Lecture 4: rref pattern, fit polynomials, matrix algebra basics
- Lecture 5: prop of matrix algebra, all bases belong to us, inverse matrix defined
- Lecture 6: elementary matrices, properties and calculation of inv. matrix
- Lecture 7: block-multiplication, (anti)symmetric matrices, concatenation
- Lecture 8: span and column calculations in Rn, intro to LI
- Lecture 9: LI and the CCP
- Lecture 9 bonus: basics of linear transformations on Rn
- Lecture 10: fundamental theorem of linear algebra (no video)
- Lecture 11: gallery of LT, injectivity and surjectivity for LT, new LT from old
- Lecture 12: examples and applications of matrices and LTs
- Lecture 13 part 1: solution to Quiz 1
- Lecture 13 part 2: solution to Quiz 1
- Lecture 14: vector space defined, examples, subspace theorem
- Lecture 15: axiomatic proofs, subspace thm proof, Null(A) and Col(A)
- Lecture 16: generating sets for spans, LI, basis and coordinates
- Lecture 17: theory of dimension and theorems on LI and spanning
- Lecture 18: basis of column and null space, solution set structure again
- Lecture 19: subspace thms for LT and unique linear extension prop
- Lecture 19.5: isomorphism is equivalence relation, finite dimension classifies
- Lecture 20 part 1: coordinate maps and matrix of LT for abstract vspace
- Lecture 20 part 2: examples of matrix of LT in abstract case (Incidentally, my intuition at the end of this about the rank of the BAB mapping is incorrect. That map does in fact have rank 4 despite being built with the rank 2 B.)
- Lecture 21: kernel vs nullspace, coordinate change
- Lecture 22 part 1: coordinate change for matrix of LT
- Lecture 22 part 2:rank nullity, Identity padded zeros thm, matrix congruence comment
- Lecture 22.5: proof of abstract rank nullity theorem, examples
- Lecture 23: part 1: quotient of vector space by subspace
- Lecture 23 part 2: quotient space examples,1st isomorphism theorem
- Lecture 24: structure of subspaces, TFAE thm for direct sums
- Lecture 25: direct sums again, gallery of 3D isomorphic vspaces
- Review for Test 2 part 1
- Review for Test 2 part 2
- Lecture 26: motivation, calculation and interpretation of determinants
- Lecture 27: determinant properties, Cramer's Rule derived
- Lecture 28: adjoint fla for inverse, eigenvectors and values
- Lecture 28 additional eigenvector examples
- interesting example for Lecture 28
- Lecture 29: basic structural theorems about eigenvectors
- supplement to Lecture 29
- Lecture 30: eigenspace decompositions, orthonormality
- concerning rotations
- Lecture 31: complex vector spaces and complexification
- Lecture 32: rotation dilation from complex evalue, GS example
- Lecture 33: orthonormal bases, projections, closest vector problem.
- Lecture 34: complex inner product space, Hermitian conjugate and properties
- Lecture 35: overview of real Jordan form, application to DEqns
- Lecture 36: invariant subspaces, triangular forms, nilpotentence
- Lecture 37: nilpotent proofs, diagrammatics for generalize evectors, A = D + N
- Lecture 38: minimal polynomial, help with homework
- Lecture 39: solution to takehome Quiz 3
- Lecture 40: partial course overview

Back to my Home

Last Modified: 1-20-2017