Feedback – part I

Without much ado here is a book that is changing my life:

And why is it changing my life? Well, the book was introduced to me in a workshop about how to make sense of student comments in regular end-of-semester evaluation forms. So, I started reading the book. The premise of the book is that there are various reasons to get triggered when one receives comments and there are different types of feedback and expecting one type and receiving another type causes some troubles. Then it goes into several examples, and many many details of it and ways to prevent some of them, solve some of them, and avoid some others. I particularly like the book because of its scientific approach to writing, but at the same time being very conversational, and my most favourite part is their examples. They provide tons of examples, many case studies, and several extreme examples.

BUT…

It is changing my life because as I keep reading it, I realize that most of communications and relations in one’s life fall into the same categories that the book describes, and one can follow the same strategies to make the whole experience of communicating with others (at any level and of any sort) much much better. To give you an idea of the big picture of the book, let me mention some of the main things:

One of the things that the book mentions is that when you receive a feedback three things can be triggered:

1. Truth:
• It is when you feel like the feedback is not representing the truth. For example, “I couldn’t be rude at that party, because I wasn’t at that party. And my name is not Mike!” This is of course one of their extreme examples that makes the whole thing make a lot of sense and I love it. In a more realistic way, I could get a comment from a student that I don’t have a sense of time! While I go to class 5 minutes early and prepare my slides and the notes, and start right at 10 O’clock to lecture. What the student is saying is not the truth. Well, at least not according to my definition. They go and discuss it in much detail that what happens when we feel the comment about us is not the truth and how we can resolve this. Some of the later chapters of the book are on the different aspects of this trigger.
2. Relationship:
• It happens when you expect some other type of point of view. For example, a student can tell that “he wasn’t organized.” and you feel like “after all that work that I put into preparing this class and the schedule that I followed to the minute, and extra problems with solutions that I posted online, you tell me this?”. Or in a different way, you might get a comment that “He knows math but he doesn’t know how to teach it!” And you’ll be like “Who the hell are you to judge my teaching?!” There are a lot to be learnt from the feedback when this gets triggered and the book spends a good amount on this topic.
3. Identity:
• It is when you feel like you don’t know who you are, e.g. “Maybe I am a bad teacher after all”, or “What am I doing with my life teaching these courses?” And there are many other things that can be done in this case too.

The book goes on to identify three types of feedback:

1. Praise:
• “Good job”, “Awesome teacher”, “Worst instructor ever” etc are examples of praise. It doesn’t have much information in it. It simply says the feedback giver is happy or not with the performance. We all need that at times and some times we receive that feedback.
2. Coaching:
• “Give us more time during the class to work on problems”, “provide more examples”, “he talks too fast (i.e. don’t talk too fast)” etc are examples of coaching. They have lots of information in them and usually these what make you grow.
• If I’m looking for praise and receive coaching instead, I’ll be maaaaaaaaaaaaad! “I’m spending lots of time preparing for this course, don’t tell me I need to use a larger size font on the worksheets!”.
• If I’m looking for coaching and receive praise instead, I’ll be alright but frustrated as I don’t know where to go next. “OK, the exam I designed was a good exam and covered all the material, but how can I make it more related to practice problems students are doing? how do I make sure that it’s not too much for 2 hours? How would I ask a reasonable question about that topic that I didn’t ask about it?”.
3. Evaluation:
• Your salary gets raised, a student sings up for your next class after they had a course with you before, you get fired, your spouse wants a divorce, you get accepted to graduate school. These are examples of evaluation.
• If I’m looking for praise and I receive evaluation instead, I’ll feel a little lost. “OK, my contract got extended, but am I doing well enough? Are you happy with me?”.
• If I’m looking for evaluation and I get praised instead, I’ll feel they’re hiding something. “Yeah, I know I’m doing a good job, will you sign my contract for next year or not, though?”.
• If I’m looking for coaching and receive evaluation instead, it’s just unsettling. “My application for this job got rejected, but you haven’t told me what are the strong and weak points on my portfolio, how do I improve it so that I get the job next year, what are the things that you were looking for ans was lacking in my application?”.
• If I’m looking for evaluation and I get coaching instead, I won’t care! “Are you gonna hire me or not? I don’t care that I should have gone to that conference!”

Then the book goes into every detail of every aspect of these topics and provides many many beautiful examples, and comes up with strategies to figure out what situation we are in and how to respond to each situation when facing them. I don’t necessarily agree with all of their strategies, and I think on several cases I can do the exact opposite of what they suggest and have a better outcome, but the book is extremely helpful in identifying these situations and classifying them.

If you gave it a try, let me know what you think about it, the whole book, or any of the single details. I’d love to discuss the topics to learn more.

Determinant and Jacobian

Today I started my linear algebra class by motivating the determinant using a little bit of history and applications. In particular I talked about determinant appearing in the change of variables in integration of multivariate functions in calculus:Then after the class I noticed that my picture of what happens when we linearize the image wasn’t very clear as I had drawn it. So I decided to take a look at an actual example. The following code takes a function $f$ that maps $\mathbb{R}^2$ to $\mathbb{R}^2$ and draws three regions:

• the unit cube in blue,
• the image of the unit cube under $f$ in red, and
• the linearization of the image by Jacobian of $f$ at the origin
def show_linearization(f,n=100):
# f is a function that maps R^2 to R^2 for example
# f(x,y) = ((x^2+2*(x+2)*(y+1))/4-1,y^2-(x+1)*y)
# The output is three areas:
# the unit cube
# the image of unit cube under f
# the linearization of the image using the Jacobian of f at (0,0)

var('x y')
A = jacobian(f,(x,y))(0,0)
p = f(0,0)
domxy = []
imgxy = []
jacxy = []
for i in range(n+1):
for j in range(n+1):
domxy.append((i/n,j/n))
imgxy.append(f(i/n,j/n))
jacxy.append(p+A*vector([i/n,j/n]))

P = points(domxy,color="blue",aspect_ratio=1, alpha = .3)
Q = points(imgxy,color="red",aspect_ratio=1, alpha = .3)
R = points(jacxy,color="green",aspect_ratio=1, alpha = .3)

(P+Q+R).show()

Here is a sample run:

f(x,y) = ((x^2+2*(x+2)*(y+1))/4-1,y^2-(x+1)*y)
show_linearization(f)

and the output is:

I chose the function in a way that $f(0,0)=(0,0)$ for simplicity. Here it is on sage cell server for you to play around with it: link.

What do you think? Any suggestions or comments?

Teaching philosophies

One of the recent workshops I’ve attended here at the Taylor Institute was about developing a teaching philosophy offered by Carol Berenson.The goals of the workshop were

• to reflect on and articulate our beliefs about teaching and learning and where they come from,
• to make connections between our beliefs and specific teaching and learning strategies, and
• prepare a draft of a teaching philosophy.

In short, a learner-centred teaching style has the following key elements in it 1-4:

1. Actively Engage Learners:
• Material are stimulating, relevant, and interesting
• Explain material Clearly
• Use a variety of methods that encourages active learning
• Adapt to evolving classroom context
2. Demonstrate Passion, Empathy, and respect:
• Show interest in students’ opinions and concerns
• Understand their diverse talents, needs, prior knowledge, and approach to learning
• Encourage interaction between students and instructor
• Share your love of the discipline
3. Communicate Clear Expectations:
• Make clear the intended learning outcomes and standards for performance
• Provide organization, structure, and direction for where the course is going
4. Encourage Student Independence:
• Provide opportunities to develop and draw upon personal interests
• Offer choice in learning processes and modes of assessment
• Provide timely and developmental feedback on learning
• Encourage metacognition to promote self-assessment
5. Create a Teaching and Learning Community:
• Encourage mutual learning
• Encourage thoughtful, respectful, and collaborative engagement and dialogue between all members of the classroom community
6. Use Appropriate Assessment methods:
• Clearly align assessment methods with intended course outcomes
• Provide clear criteria for evaluation
• Emphasize deep learning
• Scaffold assessments to ensure progressive learnin
7. Commit to Continuous Improvement:
• Gather formative and summative feedback on your teaching
• Practice critical self-reflection
• Consult scholarly literature on teaching and learning
• Identify clear goals for strengthening your teaching

One of the things that comes to mind that needs to be added to that list is to evaluate the Impact, we’ve had on students. That is, how do we know our students have “learned”.

In order to reflect on above mentioned aspects of a teaching philosophy in ours, we were asked to complete the following questionnaire. I’d be happy to see your responses if you care to share.

A teaching philosophy is a statement that clearly and logically communicates

• What your fundamental values and beliefs are about teaching and learning
• Why you hold these values and beliefs (literature and personal experience)
• It is becoming more common to cite one or two literature in teaching philosophies
• In mine, I usually refer to a few people that have great impact on my philosophy including
• Joseph Stepans on values of different teaching methodologies and importance of misconceptions
• Gilbert Strang on his approach to teaching linear algebra and what matters most is what students remember eventually
• How you translate these values and beliefs into your teaching and learning practice within the context of your discipline (contains also reflections on how your evaluate and plan to continue grow your practice)

Teaching philosophies are supposed to be 5-7:

• 1-2 pages long, single spaced
• First person, narrative, reflective, authentic voice
• Grounded in discipline, and avoiding jargon
• might use a metaphor or critical incident as a way in
• Can link to scholarly literature (speaks to the why)
• Demonstrates humility and commitment to on-going growth (one can share their challenges)
• Paints “your” picture
• and they are an ever evolving work in progress

A typical flow of a teaching philosophy is that it starts with beliefs (what do you think) and moves into the strategies (what do you do), and mentions the impact (what is the effect of these strategies on learners, self, colleagues), and usually ends with the future goals (how will you improve). The impact can be discussed more in the dossier, which could include comments from students, yourself, and colleagues. In order to collect comments from students one can refer to the evaluation forms that are done at the end of each semester. On top of that I usually ask for one or two feedbacks from students during each semester, which is more tailored to what I have in mind and also can help me modify my strategies during a semester. Here is an example of a mid-semester teaching feedback that I ask students to fill out (please do not fill out this one, but if you have suggestions on how to improve the form itself, the last page of the form is for that purpose and I would appreciate any comments):

In order to organize your thoughts to write a teaching philosophy, you can start by filling out a form like this:

Here is a PDF file for easier printing: PDF. Maybe fill a few of them and then choose between the best 3. As you are filling them out you might notice that a lot of strategies or impacts bleed into each other. In order to eventually formalize them in terms of a statement there are a few things you can do. One is instead of going by rows, go by columns! Another one is to pick the most important strategy for each key belief and do not repeat it for the other ones. One other issue might be that you have beliefs that you haven’t or can’t implement them in your classes. You can refer to these key believes as future goals.

Taylor institute has several great resources on this topic that can be found here. Most of the information that I’ve written here are in that webpage as well as several great teaching philosophy (real) samples.

Footnotes

1. Arthur Chickering and Zelda F. Gamson (1987) Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7) 3-7.
2. P. Ramsden (2003) Learning to teach in higher education: Thirteen principles for effective university teaching. New York: Routledge.
3. Maryellen Weimer (2013) Learner-centred teaching: Five key changes to practice. John Wiley & Sons.
4. Lizzio, Alf, Wilson, Keithia, Simons (2002) University students’ perceptions of the learning environment and academic outcomes: Implications for theory and practice. (Conceptual model for an effective academic environment). Studies in higher education, 27(1) 27-52.
5. Schonwetter (2002)
6. Seldin and Seldin (2010)
7. Kearns and Sullivan (2011)

Step-by-step reduction

One of the things that I always tell my students is to check their solutions when they are done solving a problem. That by itself can mean several things, depending on what the problems is. Of course after solving a system of linear equations, one would plug in the solutions into the original system of equations to see if they are satisfied. The harder part is to come up with a way to check if you’ve done the row-reduction correctly or not. One can easily use Sage to see if the reduced row echelon form is computed correctly. If A is your matrix, just input A.rref() to find the reduced row echelon form of it.

But still sometimes in the case that the answer is wrong, one might get frustrated by not trying to figure out in which step they’ve made an error, or sometimes they might just get stuck. Considering that row-reduction is a time consuming process, it is plausible to assume that some one can easily give up after a few tries. I have found this nice code in Sage that shows step-by-step Gauss-Jordan elimination process, and actually tells you what to do in each step, from here. Then I did a little bit of customization on it. Here is the final version:

# Naive Gaussian reduction
"""Describe the reduction to echelon form of the given matrix of rationals.

MATRIX  matrix of rationals   e.g., M = matrix(QQ, [[..], [..], ..])

Returns: reduced form.  Side effect: prints steps of reduction.

"""
M = copy(MATRIX)
num_rows=M.nrows()
num_cols=M.ncols()
show(M.apply_map(lambda t:t.full_simplify()))

col = 0   # all cols before this are already done
for row in range(0,num_rows):
# ?Need to swap in a nonzero entry from below
while (col < num_cols
and M[row][col] == 0):
for i in M.nonzero_positions_in_column(col):
if i > row:
print " swap row",row+1,"with row",i+1
M.swap_rows(row,i)
show(M.apply_map(lambda t:t.full_simplify()))
break
else:
col += 1

if col >= num_cols:
break

# Now guaranteed M[row][col] != 0
and M[row][col] != 1):
print " take",1/M[row][col],"times row",row+1
M.rescale_row(row,1/M[row][col])
show(M.apply_map(lambda t:t.full_simplify()))

for changed_row in range(row+1,num_rows):
if M[changed_row][col] != 0:
factor = -1*M[changed_row][col]/M[row][col]
print " take",factor,"times row",row+1,"plus row",changed_row+1
show(M.apply_map(lambda t:t.full_simplify()))
col +=1

print "Above is a row echelon form, let's keep cruising to get the reduced row echelon form:\n"

for i in range(num_rows):
row = num_rows-i-1
if M[row] != 0:
for col in range(num_cols):
if M[row,col] != 0:
if M[row,col] != 1:
print " take",1/M[row][col],"times row",row+1
M.rescale_row(row,1/M[row][col])
show(M.apply_map(lambda t:t.full_simplify()))
break

for changed_row in range(row):
factor = -1 * M[row-1-changed_row,col]
if factor != 0:
print " take", factor,"times row", row+1, "plus row", row-1-changed_row+1
show(M.apply_map(lambda t:t.full_simplify()))
return(M.apply_map(lambda t:t.full_simplify()))


And here is a sample run:

M = matrix(SR,[[3,-1,4,6],[0,1,8,0],[-2,1,0,-4]])


And the output looks like this:

Click here to run it in the sage cell server. Note that the code is implemented to work over rational field since it is being used for pedagogical reasons.

In the comments Jason Grout has added an interactive implementation of the first part of the code:

# Naive Gaussian reduction
@interact
"""Describe the reduction to echelon form of the given matrix of rationals.

M  matrix of rationals   e.g., M = matrix(QQ, [[..], [..], ..])

Returns: None.  Side effect: M is reduced.  Note: this is echelon form,
not reduced echelon form; this routine does not end the same way as does
M.echelon_form().

"""
num_rows=M.nrows()
num_cols=M.ncols()
print M

col = 0   # all cols before this are already done
for row in range(0,num_rows):
# ?Need to swap in a nonzero entry from below
while (col < num_cols                and M[row][col] == 0):              for i in M.nonzero_positions_in_column(col):                 if i > row:
print " swap row",row+1,"with row",i+1
M.swap_rows(row,i)
print M
break
else:
col += 1

if col >= num_cols:
break

# Now guaranteed M[row][col] != 0
and M[row][col] != 1):
print " take",1/M[row][col],"times row",row+1
M.rescale_row(row,1/M[row][col])
print M
change_flag=False
for changed_row in range(row+1,num_rows):
if M[changed_row][col] != 0:
change_flag=True
factor=-1*M[changed_row][col]/M[row][col]
print " take",factor,"times row",row+1,"plus row",changed_row+1
if change_flag:
print M
col +=1


One of the uses of this can be to find the inverse of a matrix step by step. For example:

var('a b c d')
A = matrix([[a,b],[c,d]])
R = gauss_method(A.augment(identity_matrix(A.ncols()),subdivide=True))

will give you: Or you can run it for a generic matrix of size 3 (for some reason it didn’t factor i, so I used letter k instead:)

var('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')
A = matrix([[a,b,c],[d,e,f],[g,h,k]])
R = gauss_method(A.augment(identity_matrix(A.ncols()),subdivide=True))

The steps are too long, so I’m not going to include a snapshot, if you are interested look at the steps here. But you can check out only the final result by

view(R.subdivision(0,1))

which will give you And to check if the denominators and the determinant of the matrix have any relations with each other you can multiply by the determinant and simplify:

view((det(A)*R.subdivision(0,1)).apply_map(lambda t:t.full_simplify()))

to get

Do you think this can help students?

Generating not-so-much-random matrices

I’ve been using sage for a while now. I’ve also been teaching linear algebra for a few years. One of the problems in teaching linear algebra is coming up with a handful of examples of matrices that can nicely be row-reduced. One way to do this is to start with your “nice” row-reduced matrix and take a few (or many) “nice” elementary matrices and there is your example. So, one might want to code this in sage. I just recently found out that actually sage has such a function built in it. For example,

random_matrix(ZZ,4,5,algorithm='echelonizable',rank=3, upper_bound=7)


will create a random $4 \times 5$ matrix with rank $3$, integer entries (that’s the ZZ), and entries no bigger than $7$, which can “nicely” be turned into rref. So, here it is in sage cell server. I think this is a good thing to share with students so they can practice as many times as they want.

For the same reasons when generating a random matrix to find its inverse, we usually want a matrix with small-ish integer entries such that the inverse also has relatively small integer entries. It is easy to see that for a matrix $A$ with integer entries to have an inverse with integer entries, it is necessary that its determinant is $\pm 1$. It turns out that it is the sufficient condition too, surprisingly, thanks to Cramer’s rule! Though it might lose some pedagogical purposes, like relating the inverse to the determinant, but here is a code that generates random matrices with integer entries where their inverses also have integer entries:

random_matrix(ZZ,3,algorithm='unimodular',upper_bound=6)

The sage documentation says:

Warning: Matrices generated are not uniformly distributed. For
unimodular matrices over finite field this function does not even
generate all of them: for example "Matrix.random(GF(3), 2,
algorithm='unimodular')" never generates "[[2,0],[0,2]]". This
function is made for teaching purposes.

And

Random matrices in echelon form.  The "algorithm='echelon_form'"
keyword, along with a requested number of non-zero rows
("num_pivots") will return a random matrix in echelon form.  When
the base ring is "QQ" the result has integer entries.  Other exact
rings may be also specified.

sage: A=random_matrix(QQ, 4, 8, algorithm='echelon_form', num_pivots=3); A # random
[ 1 -5  0 -2  0  1  1 -2]
[ 0  0  1 -5  0 -3 -1  0]
[ 0  0  0  0  1  2 -2  1]
[ 0  0  0  0  0  0  0  0]
sage: A.base_ring()
Rational Field
sage: (A.nrows(), A.ncols())
(4, 8)
sage: A in sage.matrix.matrix_space.MatrixSpace(ZZ, 4, 8)
True
sage: A.rank()
3
sage: A==A.rref()
True

For more, see the documentation of the "random_rref_matrix()"
function.  In the notebook or at the Sage command-line, first
execute the following to make this further documentation available:

from sage.matrix.constructor import random_rref_matrix

Random matrices with predictable echelon forms.  The
"algorithm='echelonizable'" keyword, along with a requested rank
("rank") and optional size control ("upper_bound") will return a
random matrix in echelon form.  When the base ring is "ZZ" or "QQ"
the result has integer entries, whose magnitudes can be limited by
the value of "upper_bound", and the echelon form of the matrix also
has integer entries.  Other exact rings may be also specified, but
there is no notion of controlling the size.  Square matrices of
full rank generated by this function always have determinant one,
and can be constructed with the "unimodular" keyword.

sage: A=random_matrix(QQ, 4, 8, algorithm='echelonizable', rank=3, upper_bound=60); A # random
sage: A.base_ring()
Rational Field
sage: (A.nrows(), A.ncols())
(4, 8)
sage: A in sage.matrix.matrix_space.MatrixSpace(ZZ, 4, 8)
True
sage: A.rank()
3
sage: all([abs(x)<60 for x in A.list()])
True
sage: A.rref() in sage.matrix.matrix_space.MatrixSpace(ZZ, 4, 8)
True

For more, see the documentation of the
"random_echelonizable_matrix()" function.  In the notebook or at
the Sage command-line, first execute the following to make this
further documentation available:

from sage.matrix.constructor import random_echelonizable_matrix
The "x" and "y" keywords can be used to distribute entries
uniformly. When both are used "x" is the minimum and "y" is one
greater than the maximum.

sage: random_matrix(ZZ, 4, 8, x=70, y=100)
[81 82 70 81 78 71 79 94]
[80 98 89 87 91 94 94 77]
[86 89 85 92 95 94 72 89]
[78 80 89 82 94 72 90 92]
If only "x" is given, then it is used as the upper bound of a range
starting at 0.

sage: random_matrix(ZZ, 5, 5, x=25)
[20 16  8  3  8]
[ 8  2  2 14  5]
[18 18 10 20 11]
[19 16 17 15  7]
[ 0 24  3 17 24]
To control the number of nonzero entries, use the "density" keyword
at a value strictly below the default of 1.0.  The "density"
keyword is used to compute the number of entries that will be
nonzero, but the same entry may be selected more than once.  So the
value provided will be an upper bound for the density of the
created matrix.  Note that for a square matrix it is only necessary
to set a single dimension.

sage: random_matrix(ZZ, 5, x=-10, y=10, density=0.75)
[-6  1  0  0  0]
[ 9  0  0  4  1]
[-6  0  0 -8  0]
[ 0  4  0  6  0]
[ 1 -9  0  0 -8]
For a matrix with low density it may be advisable to insist on a
sparse representation, as this representation is not selected
automatically.

sage: random_matrix(ZZ, 5, 5, density=0.3, sparse=True)
[ 4  0  0  0 -1]
[ 0  0  0  0 -7]
[ 0  0  2  0  0]
[ 0  0  1  0 -4]
[ 0  0  0  0  0]
Random rational matrices:
sage: random_matrix(QQ, 2, 8, num_bound=20, den_bound=4)
[ -1/2 6 13 -12 -2/3 -1/4 5 5]
[ -9/2 5/3 19 15/2 19/2 20/3 -13/4 0]
Random matrices over other rings.  Several classes of matrices have
specialized "randomize()" methods.  You can locate these with the
Sage command:

search_def('randomize')

Gershgorin Disks

Gershgorin circle theorem roughly states that the eigenvalues of an $n \times n$ matrix lie inside $n$ circles where $i$-th circle is centred at $A_{i,i}$ and its radius is the sum of the absolute values of the off-diagonal entries of the $i$-th row of $A$. Applying this to $A^{\top}$ implies that the radius of this circles shall be the smaller of the sums for rows and columns. The following Sage code draws the circles for a given matrix.

def Gershgorin(A,evals=False):
# A is a square matrix
# Output is the Gershgorin disks of A

from sage.plot.circle import Circle
from sage.plot.colors import rainbow

n = A.ncols()
Colors = rainbow(n, 'rgbtuple')
E = point([])

B = A.transpose()
R = [(A[i-1,i-1], min(sum( [abs(a) for a in (A[i-1:i]).list()] )-abs(A[i-1,i-1]),sum( [abs(a) for a in (B[i-1:i]).list()] )-abs(B[i-1,i-1]))) for i in range(1,n+1)]
C = [ circle((real(R[i][0]),imaginary(R[i][0])), R[i][1], color="black") for i in range(n)]
if evals == True:
E = point(A.eigenvalues(), color="black", size=30)
CF = [ circle((real(R[i][0]),imaginary(R[i][0])), R[i][1], rgbcolor=Colors[i], fill=True, alpha=1/n) for i in range(n)]

(sum(C)+sum(CF)+E).show()


And here is a sample output:

A =  random_matrix(CDF,4)
Gershgorin(A)

Gershgorin disks for a randomly generated $4\times 4$ complex matrix

If you want to see the actual eigenvalues, call the function as below:

Gershgorin(A,evals=True)


Some March Mathness

I went to several talks and meetings in the past few days, and I am going to talk about some of them, but first let me start with some predicting game:

A fun problem

Take 10 coins and put them in a pile. Divide the pile into two piles of arbitrary sizes and multiply the number of coins in the two piles and record that number. Repeat this with each pile and each time record the number, until you can’t divide the piles any more. Now add up all the numbers you’ve recorded. What is the number you got?

Now my job is to guess what that number is. Well let me see, you divided the first pile and then one more time, and then another time, and… emm… I see, at the end you added 9 numbers. OK, so far, so good, then you added this one with that, and… mmm… OK… let’s see… and that adds up to… 45?

The problem is very simple if you look at it correctly. The catch is that it doesn’t matter how you divide the piles, you’ll always get the same number at the end. I think this is one of the first problems that got me interested in math. I read the problem as one of the monthly problems in a math magazine aimed at middle school students. The magazine was called Borhan, and was being published by Madreseh Publications in Iran. The editor of the Magazine, Parviz Shahriari, proposed a bunch of problems in each issue, and he would give hints or solutions in the next issue. If you want to find the actual problem take a look at the first issue. There it is 25 coins and the solution uses some basic algebra. Years later I remembered the problem again but I couldn’t remember the solutions. So, I ought to solve it myself. Here is what I came up with:

WARNING: SPOILER!

Draw 10 dots on a paper and divide it into two sets of points, say 7 and 3. Now draw all the lines that connects the dots in the first set to the second set. How many lines you’ve got? Yes, $7 \times 3$. Now keep doing this with each of the two sets of 3 and 7 dots. At the time that you can’t divide any more, you have drawn all possible lines between every pair of points. Adding up all the results of multiplications is just counting the number of lines. As you can see, it didn’t matter how you divided them. The question now is how many lines are there. Let’s number the dots: 1,2,3, …, 10. There are 9 lines that pass through 1. Put them away. Now there are 8 lines that pass through 2, put them away. Repeat this until the dot number 9, there is exactly one line. So, the total number of lines is 1+2+3+ … + 9 = 45. OK. It’s easy to do it for 10, but what if I started with 100 points? What is 1+2+3+…+99? Well, it is a famous problem due to Gauss. People say when he was in the school and his lazy teacher wanted to keep them busy after teaching them how to add, so that he could take a nap, he gives them the problem of adding all the numbers from 1 to 100. The Gauss comes up with this idea:

1 + 2 + … + 99 + 100 = S

100 + 99 + … + 2 + 1 = S

———————

101 + 101 + … + 101 + 101 = 2S

S0, there are 100 numbers that are added together and all of them are 101. The sum is 10100. But that’s twice the sum we are looking for. So let’s divide it by 2. The sum is 5050. The fact that Gauss knew how to multiply and divide right after learning how to add is not verified by anyone yet! So, now you tell me, what do you if you did it with 25 coins?

Project-based calculus

One of the talks was about teaching calculus with projects, by Peter Zizler from Mount Royal University. There are a lot of people talking about this and there are good resources available for launching a calculus course that is entirely/mostly based on projects. To list a few

Linear Markov Chain Markov Fields

Another talk was on Markov Chain Markov Fields by University of Calgary’s own Deniz Sezer. The talk was a review on the paper “Markov Chain Markov Field dynamics: Models and statistics” by Xavier Guyon and Cécile Hardouin, 2002 (DOI: 10.1080/02331880213192). The idea is that when dealing with Markov processes people tend to ignore the structure, because a lot of times it’s just so complicated that you really don’t know much about them. On the other hand, if you know something about the structure of what interacts with what, then you can tell a lot more about the behaviour of the system. In this talk, the focus was on the cases that only pairs interact with each other, and hence you can look at a graph. A neighbourhood of a set is defined and some properties such as ergodicity and strong law of large numbers are explored. A couple of interesting things mentioned were the notion of conditional independence of two sets $A$ and $B$ given a set $C$. This notion is related to edges of a graph to be present or not, and disconnectedness of the graph, though it wasn’t really mentioned in the talk. Another interesting concept is a set of variables $Y_A$ is called Markovian, if $A$ and $A^c$ are conditionally independent given $\partial A$ (neighbourhood of $A$). Then a theorem says

Theorem. A set of variables is Markovian if and only if all active sets in it are simplicies.

I wondered about the cases when more than just pairs can interact with each other, and hence we need something more to talk about the structure of the system, like a simplex. Apparently some few studies are done in that direction and Deniz thinks some might be found in the book “Guyon, X. (1995). Randon Fields on a Network: Modelling, Statistics and Applications (Springer)”.

Low-rank matrix completions in geophysics

Mauricio D. Sacchi, Professor of geophysics at University of Alberta gave this interesting talk on New and not-so-new applications of low-rank matrix and tensor completions to seismic data processing. He started by mentioning the Julia Seismic software. He explained thath the Imaging is about “where” and inversion is about “what”. Then he suggested to look at the 5-dimensional data regularization. The 5 dimensions come up as 2 dimensions from the receiver, 2 dimensions from the source, and 1 is natural to be time, but it actually is the frequency. In the Parsimonious model you want to have some sparsity/simplicity, predictability, and  rank to be small (where tensor completion methods and Hankel methods play a role). The problem is that the algorithms around require regular and dense data. The solutions is to use linear and multilinear algebraic inverse theory. But there are infinitely many solutions, hence we need to put constraints (sparsity, stability, rank etc.) to get unique solutions. For example if you have

$\begin{bmatrix} d_1 \\ 0 \\ d_3 \\ d_4 \end{bmatrix} = \begin{bmatrix} 1 \\ & 0 \\ && 1 \\ &&& 1 \end{bmatrix} \begin{bmatrix} x_1 \\ x_2 \\ x_3 \\ x_4 \end{bmatrix}$,

where $d_i$s are observed data and $x_i$s are the true data, you can assume that $x = Fc$ for some Fourier transform $F$ and a sparse $c$. Then $d = SFc$. This tells us that signals that admit a sparse representation are important.

• Levy, Walker Ulrych, and Fullagar in 1982 used linear programming to solve the problem but that method is old now.
• Richard Baraniuk in 2007 used compressed sensing, but he had to assume random sampling.
• Lustig Donoho, and Pauly in 2007 also used compressed sensing for rapid MRI imaging (sparse MRI).

The problem with all of them is that they cannot guarantee e.g. 99% recovery of data.

On the other hand there is always noise. Actually the noise increases the rank, so people use rank-reduction for de-noising the data. One of the most common ways for rank reduction is by using SVD (singular value decomposition) and keeping only the largest singular values. If you find a low-rank approximation then you will have

$M^{obs} = SM$,

$M^k = M^{obs} + (I-S) R [M^{k-1}]$.