Another mathematician visits the ballpark – OPS

Yes, I more-or-less stole the above title from the 2004 Ken Ross book entitled A Mathematician at the Ballpark. Like that book, anyone familiar with middle-school (or junior high school) math, should have no problem with most of what we do here. However, I will try to go into baseball in more detail than the book did.

Paraphrasing slightly, I read somewhere the following facetious remark:

From a survey of 1000 random baseball fans 

across the nation,  183% of them hate math. 

If you are one of these 183%, then this series could be for you. Hopefully, even if you aren’t a baseball expert, but you would like to learn some baseball statistics, (now often called “sabermetrics”), these posts will help. I’m no expert myself, so we’ll learn together.

In this series of blog posts, each post will introduce a particular metric in baseball statistics as well as some of the math and baseball behind it. We illustrate all these notions using the Baltimore Orioles’ 2022 season.

This week we look at one of the most popular statistics you see on televised games: OPS or “On-base Plus Slugging,” which is short for on-base percentage plus slugging percentage. Don’t worry, we’ll explain all these terms as we go. 

On-base percentage

First, On-Base Percentage or OBP is a more recent version of On-Base Average or OBA (the same as OBP but the SF term is omitted). We define 

OBP = (H + BB + HBP)/(AB + BB + HBP + SF), 

where 

  • H is the number of Hits (the times the batter reaches base because of a batted, fair ball without error by the defense), 
  • BB is the number of Base-on-Balls (or walks), where a batter receives four pitches that the umpire calls balls, and is in turn awarded first base,
  • HBP, or Hit By Pitch, counts the times this hitter is touched by a pitch and awarded first base as a result, and 
  • SF is the number of Sacrifice Flies and AB the number of At-Bats, which are more complicated to carefully define.

The official scorer keeps track of all these numbers, and more, as the baseball game is played. We still have to define the expressions AB and SF.

First, SF, or Sacrifice Flies, counts the number of fly balls hit to the outfield for which both of the following are true:

  • this fly is caught for an out, and a baserunner scores after the catch (so there must be at most one hit at the time),
  • the fly is dropped, and a runner scores, if in the scorer’s judgment the runner could have scored after the catch had the fly ball been caught.

A sacrifice fly is only credited if a runner scores on the play. (By the way, this is a “recent” statistic, as they weren’t tabulated before 1954. Between 1876, when the major league baseball national league was born, and 1954 baseball analysts used the OBA instead.)

Second, AB, or At-Bats, counts those plate appearances that are not one of the following:

  • A walk,
  • being hit by a pitch,
  • a bunt (or Sacrifice Hit, SH),
  • a sacrifice fly,
  • interference (the catcher hitting the bat with his glove, for example), or
  • an obstruction (by the first baseman blocking the base path, for example).

Incidentally, the self-explanatory number Plate Appearances, or PA, can differ from AB by as much as 10%, mostly due to the number of walks that a batter can draw.

The main terms in the OBP expression are H and AB. So we naturally expect OBP to be approximately equal to the Batting Average, defined by

BA = H/AB,

For example, if we take the top 18 Orioles players and plot the BA vs the OBP, we get the following graph:

The line shown above is simply the line of best fit to visually indicate the correlation.

Example: As an example, let’s look at the Orioles’ All-Star center fielder,  Curtis Mullins, who had 672 plate appearances and 608 at bats, for a difference of 672 − 608 = 64. He had 1 bunt, 5 sacrifice flies, he was hit by a pitch 9 times, and walked 47 times. These add up to 62, so (using the above definition of AB) the number of times he was awarded 1st base due to interference or obstruction was 64 − 62 = 2.

Mullins’ H = 157 hits break down into 105 singles, 32 doubles, 4 triples, and 16 home runs.

Second, let’s add to these his 126 strikeouts, for a total of 157+126+64 = 347.

The remaining 608 − 347 = 261 plate appearances were pitches hit by Mullins, but either caught on the fly but a fielder or the ball landed fair and Mullins was thrown out at a base.

These account for all of Mullins’ plate appearances. Mullins has a batting average of BA = 157/608 = 0.258 and an on-base percentage of OBP = 0.318.

Slugging percentage

The slugging percentage, SLG, (SLuGging) is the total bases achieved on hits divided by at-bats:

SLG = TB/AB.

Here, TB or Total Bases, is the weighted sum

TB = 1B + 2*2B + 3*3B + 4*HR,

where

  • 1B is the number of “singles” (hits where the batter makes it to 1st Base),
  • 2B is the number of doubles,
  • 3B is the number of triples, and
  • HR denotes the number of Home Runs.

On-base Plus Slugging

With all these definitions under own belt, finally we are ready to compute “on-base plus slugging”, that is the on-base percentage plus slugging percentage:

OPS = OBP + SLG.

Example: Again, let’s consider Curtis Mullins. He had 1B = 105 singles, 2B = 32 doubles, 3B = 4 triples, and HR = 16 home runs, so his TB = 105+64+12+64 = 245. Therefore, his SLG = 245/608 = 0.403, so his on-base plus slugging is OPS = OBP + SLG = 0.318 + 0.403 = 0.721.

This finishes our discussion of OPS. I hope this helps explain it better. For more, see the OPS page at the MLB site or the wikipedia page for OPS

Let’s do the Landau shuffle

Here’s a shuffle I’ve not seen before:

  1. Take an ordinary deck of 52 cards and place them, face up, in the following pattern:
    Going from the top of the deck to the bottom, placing cards down left-to-right, put 13 cards in the top row:
    \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\
    11 cards in the next row:
    \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\
    then 9 cards in the next row:
    \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\
    then 7 cards in the next row:
    \Box\ \Box\ \Box\ \Box\ \Box\ \Box\ \Box\
    then 5 cards in the next row:
    \Box\ \Box\ \Box\ \Box\ \Box\
    then 3 cards in the next row:
    \Box\ \Box\ \Box\
    and finally, the remaining 4 cards in the last row:
    \Box\ \Box\ \Box\ \Box\
  2. Now, take the left-most card in each row and move it to the right of the others (effectively, this is a cyclic shift of that row of cards to the left).
  3. Finally, reassemble the deck by reversing the order of the placement.

In memory of the great German mathematician Edmund Landau (1877-1938, see also this bio), I call this the Landau shuffle. As with any card shuffle, this shuffle permutes the original ordering of the cards. To restore the deck to it’s original ordering you must perform this shuffle exactly 180180 times. (By the way, this is called the order of the shuffle.) Yes, one hundred eighty thousand, one hundred and eighty times. Moreover, no other shuffle than this Landau shuffle will require more repetitions to restore the deck. So, in some sense, the Landau shuffle is the shuffle that most effectively rearranges the cards.

Now suppose we have a deck of n (distictly labeled) cards, where n>2 is an integer. The collection of all possible shuffles, or permutations, of this deck is denoted S_n and called the symmetric group. The above discussion leads naturally to the following question(s).

Question: What is the largest possible order of a shuffle of this deck (and how do you construct it)?

This requires a tiny bit of group theory. You only need to know that any permutation of n symbols (such as the card deck above) can be decomposed into a composition or product) of disjoint cycles. To compute the order of an element g \in S_n, write that element g in disjoint cycle notation. Denote the lengths of the disjoint cycles occurring in g as k_1, k_2, \dots , k_m, where k_i>0 are integers forming a partition of n: n = k_1 + k_2 +\dots + k_m. Then the order of g is known to be given by order(g) = LCM(k_1, k_2, ...., k_m), where LCM denotes the least common multiple.

The Landau function g(n) is the function that returns the maximum possible order of an element g \in S_n. Landau introduced this function in a 1903 paper where he also proved the asymptotic relation \lim_{n\to \infty} \frac{\ln(g(n))}{\sqrt{n\ln(n)}}=1.

Example: If n=52 then note 52 = 13+11+9+7+5+3+4 and that LCM(13,11,9,77,5,3,4)=180180.

Oddly, my favorite mathematical software program SageMath does not have an implementation of the Landau function, so we end with some SageMath code.

def landau_function(n):
L = Partitions(n).list()
lcms = [lcm(P) for P in L]
return max(lcms)

Here is an example (the time is included to show this took about 2 seconds on my rather old mac laptop):

sage: time landau_function(52)
CPU times: user 1.91 s, sys: 56.1 ms, total: 1.97 s
Wall time: 1.97 s
180180

Coding Theory and Cryptography

This was once posted on my USNA webpage. Since I’ve retired, I’m going to repost it here.

Coding Theory and Cryptography:
From Enigma and Geheimschreiber to Quantum Theory

(David Joyner, ed.) Springer-Verlag, 2000.
ISBN 3-540-66336-3

Summary: These are the proceedings of the “Cryptoday” Conference on Coding Theory, Cryptography, and Number Theory held at the U. S. Naval Academy during October 25-26, 1998. This book concerns elementary and advanced aspects of coding theory and cryptography. The coding theory contributions deal mostly with algebraic coding theory. Some of these papers are expository, whereas others are the result of original research. The emphasis is on geometric Goppa codes, but there is also a paper on codes arising from combinatorial constructions. There are both, historical and mathematical papers on cryptography.
Several of the contributions on cryptography describe the work done by the British and their allies during World War II to crack the German and Japanese ciphers. Some mathematical aspects of the Enigma rotor machine and more recent research on quantum cryptography are described. Moreover, there are two papers concerned with the RSA cryptosystem and related number-theoretic issues.

Chapters

  • Reminiscences and Reflections of a Codebreaker, by Peter Hilton pdf
  • FISH and I, by W. T. Tutte pdf
  • Sturgeon, The FISH BP Never Really Caught, by Frode Weierud, pdf
  • ENIGMA and PURPLE: How the Allies Broke German and Japanese Codes During the War, by David A. Hatch pdf
  • The Geheimschreiber Secret, by Lars Ulfving, Frode Weierud pdf
  • The RSA Public Key Cryptosystem, by William P. Wardlaw pdf
  • Number Theory and Cryptography (using Maple), by John Cosgrave pdf
  • A Talk on Quantum Cryptography or How Alice Outwits Eve, by Samuel J. Lomonaco, Jr. pdf
  • The Rigidity Theorems of Hamada and Ohmori, Revisited, by T. S. Michael pdf
  • Counting Prime Divisors on Elliptic Curves and Multiplication in Finite Fields, by M. Amin Shokrollahi pdf,
  • On Cyclic MDS-Codes, by M. Amin Shokrollahi pdf
  • Computing Roots of Polynomials over Function Fields of Curves, by Shuhong Gao, M. Amin Shokrollahi pdf
  • Remarks on codes from modular curves: MAPLE applications, by David Joyner and Salahoddin Shokranian, pdf

For more cryptologic history, see the National Cryptologic Museum.


This is now out of print and will not be reprinted (as far as I know). The above pdf files are posted by written permission. I thank Springer-Verlag for this.

A mathematical card trick

If you search hard enough on the internet you’ll discover a pamphlet from the 1898 by Si Stebbins entitled “Card tricks and the way they are performed” (which I’ll denote by [S98] for simplicity). In it you’ll find the “Si Stebbins system” which he claims is entirely his own invention. I’m no magician, by from what I can dig up on Magicpedia, Si Stebbins’ real name is William Henry Coffrin (May 4 1867 — October 12 1950), born in Claremont New Hampshire. The system presented below was taught to Si by a Syrian magician named Selim Cid that Si sometimes worked with. However, this system below seems to have been known by Italian card magicians in the late 1500’s. In any case, this blog post is devoted to discussing parts of the pamphlet [S98] from the mathematical perspective.

In stacking the cards (face down) put the 6 of Hearts 6 \heartsuit first, the 9 of Spades 9 \spadesuit next (so it is below the 6 \heartsuit in the deck), and so on to the end, reading across left to right as indicted in the table below (BTW, the pamphlet [S98] uses the reversed ordering.) My guess is that with this ordering of the deck — spacing the cards 3 apart — it still looks random at first glance.

Hearts \heartsuitSpades \spadesuitDiamonds \diamondsuitClubs \clubsuit
69Queen2
58JackAce
4710King
369Queen
258Jack
Ace4710
King369
Queen258
JackAce47
10King36
9Queen25
8JackAce4
710King3
Si Stebbins’ System

Next, I’ll present a more mathematical version of this system to illustrate it’s connections with group theory.

We follow the ordering suggested by the mnemonic CHaSeD, we identify the suits with numbers as follows: Clubs is 0, Hearts is 1, Spades is 2 and Diamonds is 3. Therefore, the suits may be identified with the additive group of integers (mod 4), namely: {\bf{Z}}/4{\bf{Z}} = \{ 0,1,2,3 \}.

For the ranks, identify King with 0, Ace with 1, 2 with 2, \dots, 10 with 10, Jack with 11, Queen with 12. Therefore, the ranks may be identified with the additive group of integers (mod 13), namely: {\bf{Z}}/13{\bf{Z}}=\{ 0,1,2,\dots 12\}.

Rearranging the columns slightly, we have the following table, equivalent to the one above.

0123
36912
25811
14710
0369
12258
11147
10036
91225
81114
71003
69122
58111
47100
Mathematical version of the Si Stebbins Stack

In this way, we identify the card deck with the abelian group

G = {\bf{Z}}/4{\bf{Z}} \times {\bf{Z}}/13{\bf{Z}} .

For example, if you spot the 2 \clubsuit then you know that 13 cards later (and If you reach the end of the deck, continue counting with the top card) is the 2 \heartsuit, 13 cards after that is the 2 \spadesuit, and 13 cards after that is the 2 \diamondsuit.

Here are some rules this system satisfies:

Rule 1 “Shuffling”: Never riff shuffle or mix the cards. Instead, split the deck in two, the “bottom half” as the left stack and the “top half” as the right stack. Take the left stack and place it on the right one. This has the effect of rotating the deck but preserving the ordering. Do this as many times as you like. Mathematically, each such cut adds an element of the group G to each element of the deck. Some people call this a “false shuffle” of “false cut.”

Rule 2 “Rank position”: The corresponding ranks of successive cards in the deck differs by 3.

Rule 3 “Suit position”: Every card of the same denomination is 13 cards apart and runs in the same order of suits as in the CHaSeD mnemonic, namely, Clubs \clubsuit, Hearts \heartsuit, Spades \spadesuit, Diamonds \diamondsuit.

At least, we can give a few simple card tricks based on this system.

Trick 1: A player picks a card from the deck, keeps it hidden from you. You name that card.

This trick can be set up in more than one way. For example, you can either

(a) spread the cards out behind the back in such a manner that when the card is drawn you can separate the deck at that point bringing the two parts in front of you, say a “top” and a “bottom” stack, or

(b) give the deck to the player, let them pick a card at random, which separates the deck into two stacks, say a “top” and a “bottom” stack, and have the player return the stacks separately.

You know that the card the player has drawn is the card following the bottom card of the top stack. If the card on the bottom of the top stack is denoted (a,\alpha) \in G and the card drawn is (b,\beta) then

b \equiv a+3 \pmod{13}, \ \ \ \ \ \ \beta \equiv \alpha +1 \pmod{4}.

For example, a player draws a card and you find that the bottom card is the 9 \diamondsuit. What is the card the player picked?

solution: Use the first congruence listed: add 3 to 9, which is 12 or the Queen. Use the second congruence listed: add one to Diamond \diamondsuit (which is 3) to get 0 \pmod 4 (which is Clubs \clubsuit). The card drawn is the Q \clubsuit.

Trick 2: Run through the deck of cards (face down) one at a time until the player tells you to stop. Name the card you were asked to stop on.

Place cards behind the back first taking notice what the bottom card is. To get the top card, add 3 to the rank of the bottom card, add 1 to the suit of the bottom card. As you run through the deck you silently say the name of the next card (adding 3 to the rank and 1 to the suit each time). Therefore, you know the card you are asked to stop on, as you are naming them to yourself as you go along.

Expected maximums and fun with Faulhaber’s formula

A recent Futility Closet post inspired this one. There, Greg Ross mentioned a 2020 paper by P Sullivan titled “Is the Last Banana Game Fair?” in Mathematics Teacher. (BTW, it’s behind a paywall and I haven’t seen that paper).

Suppose Alice and Bob don’t want to share a banana. They each have a fair 6-sided die to throw. To decide who gets the banana, each of them rolls their die. If the largest number rolled is a 1, 2, 3, or 4, then Alice wins the banana. If the largest number rolled is a 5 or 6, then Bob wins. This is the last banana game. In this post, I’m not going to discuss the last banana game specifically, but instead look at a related question.

Let’s define things more generally. Let I_n=\{1,2,...,n\}, let X,Y be two independent, uniform random variables taken from I_n, and let Z=max(X,Y). The last banana game concerns the case n=6. Here, I’m interested in investigating the question: What is E(Z)?

Computing this isn’t hard. By definition of independent and max, we have
P(Z\leq z)=P(X\leq z)P(Y\leq z).
Since P(X\leq z)=P(Y\leq z)={\frac{z}{n}}, we have
P(Z\leq z)={\frac{z^2}{n^2}}.
The expected value of Z is defined as \sum kP(Z=k), but there’s a handy-dandy formula we can use instead:
E(Z)=\sum_{k=0}^{n-1} P(Z>k)=\sum_{k=0}^{n-1}[1-P(Z\leq k)].
Now we use the previous computation to get
E(Z)=n-{\frac{1}{n^2}}\sum_{k=1}^{n-1}k^2=n-{\frac{1}{n^2}}{\frac{(n-1)n}{6}}={\frac{2}{3}}n+{\frac{1}{2}}-{\frac{1}{6n}}.
This solves the problem as stated. But this method generalizes in a straightforward way to selecting m independent r.v.s in I_n, so let’s keep going.

First, let’s pause for some background and history. Notice how, in the last step above, we needed to know the formula for the sum of the squares of the first n consecutive positive integers? When we generalize this to selecting m integers, we need to know the formula for the sum of the m-th powers of the first n consecutive positive integers. This leads to the following topic.

Faulhaber polynomials are, for this post (apparently the terminology is not standardized) the sequence of polynomials F_m(n) of degree m+1 in the variable n that gives the value of the sum of the m-th powers of the first n consecutive positive integers:

\sum_{k=1}^{n} k^m=F_m(n).

(It is not immediately obvious that they exist for all integers m\geq 1 but they do and Faulhaber’s results establish this existence.) These polynomials were discovered by (German) mathematician Johann Faulhaber in the early 1600s, over 400 years ago. He computed them for “small” values of m and also discovered a sort of recursive formula relating F_{2\ell +1}(n) to F_{2\ell}(n). It was about 100 years later, in the early 1700s, that (Swiss) mathematician Jacob Bernoulli, who referenced Faulhaber, gave an explicit formula for these polynomials in terms of the now-famous Bernoulli numbers. Incidentally, Bernoulli numbers were discovered independently around the same time by (Japanese) mathematician Seki Takakazu. Concerning the Faulhaber polys, we have
F_1(n)={\frac{n(n+1)}{2}},
F_2(n)={\frac{n(n+1)(2n+1)}{6}},
and in general,
F_m(n)={\frac{n^{m+1}}{m+1}}+{\frac{n^m}{2}}+ lower order terms.

With this background aside, we return to the main topic of this post. Let I_n=\{1,2,...,n\}, let X_1,X_2,...,x_m be m independent, uniform random variables taken from I_n, and let Z=max(X_1,X_2,...,X_m). Again we ask: What is E(Z)? The above computation in the m=2 case generalizes to:

E(Z)=n-{\frac{1}{n^m}}\sum_{k=1}^{n-1}k^m=n-{\frac{1}{n^m}}F_m(n-1).

For m fixed and n “sufficiently large”, we have

E(Z)={\frac{m}{m+1}}n+O(1).

I find this to be an intuitively satisfying result. The max of a bunch of independently chosen integers taken from I_n should get closer and closer to n as (the fixed) m gets larger and larger.

Differential equations and SageMath

The files below were on my teaching page when I was a college teacher. Since I retired, they disappeared. Samuel Lelièvre found an archived copy on the web, so I’m posting them here.

The files are licensed under the Attribution-ShareAlike Creative Commons license.

  1. Partial fractions handout, pdf
  2. Introduction to matrix determinants handout, pdf
  3. Impulse-response handout, pdf
  4. Introduction to ODEs, pdf
  5. Initial value problems, pdf
  6. Existence and uniqueness, pdf
  7. Euler’s method for numerically approximating solutions to DEs, pdf.
    Includes both 1st order DE case (with Euler and improved Euler) and higher order DE and systems of DEs cases, without improved Euler.
  8. Direction fields and isoclines, pdf
  9. 1st order ODEs, separable and linear cases, pdf
  10. A falling body problem in Newtonian mechanics, pdf
  11. A mixing problem, pdf
  12. Linear ODEs, I, pdf
  13. Linear ODEs, II, pdf
  14. Undetermined coefficients for non-homogeneous 2nd order constant coefficient ODEs, pdf
  15. Variation of parameters for non-homogeneous 2nd order constant coefficient ODEs, pdf.
  16. Annihilator method for non-homogeneous 2nd order constant coefficient ODEs, pdf.
    I found students preferred (the more-or-less equivalent) undetermined coefficient method, so didn’t put much effort into these notes.
  17. Springs, I, pdf
  18. Springs, II, pdf
  19. Springs, III, pdf
  20. LRC circuits, pdf
  21. Power series methods, I, pdf
  22. Power series methods, II, pdf
  23. Introduction to Laplace transform methods, I, pdf
  24. Introduction to Laplace transform methods, II, pdf
  25. Lanchester’s equations modeling the battle between two armies, pdf
  26. Row reduction/Gauss elimination method for systems of linear equations, pdf.
  27. Eigenvalue method for homogeneous constant coefficient 2×2 systems of 1st order ODEs, pdf.
  28. Variation of parameters for first order non-homogeneous linear constant coefficient systems of ODEs, pdf.
  29. Electrical networks using Laplace transforms, pdf
  30. Separation of variables and the Transport PDE, pdf
  31. Fourier series, pdf.
  32. one-dimensional heat equation using Fourier series, pdf.
  33. one-dimensional wave equation using Fourier series, pdf.
  34. one-dimensional Schroedinger’s wave equation for a “free particle in a box” using Fourier series, pdf.
  35. All these lectures collected as one pdf (216 pages).
    While licensed Attribution-ShareAlike CC, in the US this book is in the public domain, as it was written while I was a US federal government employee as part of my official duties. A warning – it has lots of typos. The latest version, written with Marshall Hampton, is a JHUP book, much more polished, available on amazon and the JHUP website. Google “Introduction to Differential Equations Using Sage”.

Course review: pdf

Love, War, and Zombies, pdf
This set of slides is of a lecture I would give if there was enough time towards the end of the semester

Integral Calculus and SageMath

Long ago, using LaTeX I assembled a book on Calculus II (integral calculus), based on notes of mine, Dale Hoffman (which was written in word), and William Stein. I ran out of energy to finish it and the source files mostly disappeared from my HD. Recently, Samuel Lelièvre found a copy of the pdf of this book on the internet (you can download it here). It’s licensed under a Creative Commons Attribution 3.0 United States License. You are free to print, use, mix or modify these materials as long as you credit the original authors.

Table of Contents

0 Preface

1 The Integral
1.1 Area
1.2 Some applications of area
1.2.1 Total accumulation as “area”
1.2.2 Problems
1.3 Sigma notation and Riemann sums
1.3.1 Sums of areas of rectangles
1.3.2 Area under a curve: Riemann sums
1.3.3 Two special Riemann sums: lower and upper sums
1.3.4 Problems
1.3.5 The trapezoidal rule
1.3.6 Simpson’s rule and Sage
1.3.7 Trapezoidal vs. Simpson: Which Method Is Best?
1.4 The definite integral
1.4.1 The Fundamental Theorem of Calculus
1.4.2 Problems
1.4.3 Properties of the definite integral
1.4.4 Problems
1.5 Areas, integrals, and antiderivatives
1.5.1 Integrals, Antiderivatives, and Applications
1.5.2 Indefinite Integrals and net change
1.5.3 Physical Intuition
1.5.4 Problems
1.6 Substitution and Symmetry
1.6.1 The Substitution Rule
1.6.2 Substitution and definite integrals
1.6.3 Symmetry
1.6.4 Problems

2 Applications
2.1 Applications of the integral to area
2.1.1 Using integration to determine areas
2.2 Computing Volumes of Surfaces of Revolution
2.2.1 Disc method
2.2.2 Shell method
2.2.3 Problems
2.3 Average Values
2.3.1 Problems
2.4 Moments and centers of mass
2.4.1 Point Masses
2.4.2 Center of mass of a region in the plane
2.4.3 x-bar For A Region
2.4.4 y-bar For a Region
2.4.5 Theorems of Pappus
2.5 Arc lengths
2.5.1 2-D Arc length
2.5.2 3-D Arc length

3 Polar coordinates and trigonometric integrals
3.1 Polar Coordinates
3.2 Areas in Polar Coordinates
3.3 Complex Numbers
3.3.1 Polar Form
3.4 Complex Exponentials and Trigonometric Identities
3.4.1 Trigonometry and Complex Exponentials
3.5 Integrals of Trigonometric Functions
3.5.1 Some Remarks on Using Complex-Valued Functions

4 Integration techniques
4.1 Trigonometric Substitutions
4.2 Integration by Parts
4.2.1 More General Uses of Integration By Parts
4.3 Factoring Polynomials
4.4 Partial Fractions
4.5 Integration of Rational Functions Using Partial Fractions
4.6 Improper Integrals
4.6.1 Convergence, Divergence, and Comparison

5 Sequences and Series
5.1 Sequences
5.2 Series
5.3 The Integral and Comparison Tests
5.3.1 Estimating the Sum of a Series
5.4 Tests for Convergence
5.4.1 The Comparison Test
5.4.2 Absolute and Conditional Convergence
5.4.3 The Ratio Test
5.4.4 The Root Test
5.5 Power Series
5.5.1 Shift the Origin
5.5.2 Convergence of Power Series
5.6 Taylor Series
5.7 Applications of Taylor Series
5.7.1 Estimation of Taylor Series

6 Some Differential Equations
6.1 Separable Equations
6.2 Logistic Equation

7 Appendix: Integral tables

Problem of the Week, #121

A former colleague Bill Wardlaw (March 3, 1936-January 2, 2013) used to create a “Problem of the Week” for his students, giving a prize of a cookie if they could solve it. Here is one of them.

Problem 121

The Maryland “Big Game” lottery is played by selecting 5 different numbers in \{ 1,2,3,\dots, 50\} and then selecting one of the numbers in \{ 1,2,3,\dots, 36\}. The first section is an unordered selection without replacement (so, arrange them in increasing order if you like) but the second selection can repeat one of the 5 numbers initially picked.

How many ways can this be done?

Dodecahedral Faces of M12

 

Dodecahedral Faces of M12

 

by Ann Luers Casey

 

This post constitutes part of the math honors thesis written in spring 1997 at the USNA, advised by David Joyner. It is in the public domain.

Groups are objects in mathematics that measure symmetry in nature. A group is a set with a binary operation that has an inverse, an identity and is associative. For example, a clock has 12-fold symmetry. A more unusual group is a sporadic, non-abelian simple group. It can be very interesting to look more closely at such a group that arises naturally. One such group is M12. This post explores two different ways of creating M12 and then looks at twelve different ways M12 appears in mathematics, hence the pun the “dodecahedral faces” in the title. Specifically, this post relates M12 to the Mongean shuffle, hexads of a Steiner system, Golay codes, the Hadamard matrix of order 12, 5-transitivity, presentations, crossing the Rubicon, the minimog, the kitten, mathematical blackjack, sporadic groups, and the stabilizer in M24 of a dodecad.

Definitions:

Homomorphism: Let G1, G2 be groups with *1 denoting the group operation for G1 and *2 the group operation for G2. A function f : G1–>G2 is a homomorphism if and only if for all a,b, in Gwe have

f(a *1 b) = f(a) *2 f(b).

Isomorphism: If a homomorphism is bijective, then it is called an
isomorphism.

Automorphism: An isomorphism from a group G to itself is an automorphism.

Notation:

  • Let Fq denote the finite field with q elements, q is a power of a prime.
  • Z = the invertible scalar 2×2 matrices with entries in Fqx.
  • Let PGL2(Fq) = GL2(Fq)/Z = {A*Z | A is in GL2(Fq)}, with multiplication given by
    (A*Z)(B*Z) = (A*B)Z. This is the projective linear group over Fq.
  • LF(Fq) is the group of linear fractional transformations x–>(ax+b)/(cx+d).

Claim: There is a group theoretic isomorphism between PGL2(Fq) and LF(Fq). (See [11], Theorem 9.47 for a proof.)

Claim: LF(Fq) acts 3-transitively on the set P1(Fq) (q>3). I.e., one can send any triple to any other triple in P1(Fq) by using a suitable linear fractional transformation. (See [11], Theorem 9.48 for a proof.)

Theorem

PSL2(Fq) = < x–>x+1, x–>kx, x–>-1/x>, where k is any element in Fqthat generates the multiplicative group of squares.

For a proof, see [12], ch 10, section 1.

One way to construct the Mathieu group M12 is the following, accredited to Conway.

M12 = < PSL2(F11), (2 10)(3 4)(5 9)(6 7) >.More explicitly, let

  • f1 be a cyclic permutation = x–> x+1 = (0,1,2,…,10)(inf).
  • f2 = x–>kx = (0)(1 3 9 5 4)(2 6 7 10 8)(inf) when k=3.
  • f3 = x–>-1/x = (0 inf)(1 10)(2 5)(3 7)(4 8)(6 9).
  • f4 = (2 10)(3 4)(5 9)(6 7).

Then M12 = < f1, f2, f3, f4 >. Therefore, M12 is a subgroup of the symmetric group on 12
symbols, namely inf, 0, 1, …, 10.

Another way to construct M12 is given later under 5-transitivity.

There are many occurrences of M12 in mathematics, but here I will list and explain twelve of them:

  1. Mongean Shuffle
  2. Steiner Hexad
  3. Golay Code
  4. Hadamard Matrices
  5. 5- Transitivity
  6. Presentations
  7. Crossing the Rubicon
  8. <a href="m_12.htm#M12 and the Minimog”>M12 and the Minimog
  9. Kitten
  10. Mathematical Blackjack or Mathieu’s 21
  11. Sporadic Groups
  12. <a href="m_12.htm#Stabilizer in M24 of a dodecad”>Stabilizer in M24 of a dodecad

    1. Mongean Shuffle

     

    The Mongean shuffle concerns a deck of twelve cards, labeled 0 through 11. The permutation

    r(t) = 11-t

    reverses the cards around. The permutation

    s(t) = min(2t,23-2t)

    is called the Mongean Shuffle. The permutation group M12 is generated by r and s: M12 = < r,s >, as a subgroup of S12. (See [12], Chap. 11, Sec. 17 or [18])

    2. Steiner Hexad

     

    Jacob Steiner (1796-1863) was a Swiss mathematician specializing in projective goemetry. (It is said that he did not learn to read or write until the age of 14 and only started attending school at the age of 18.) The origins of “Steiner systems” are rooted in problems of plane geometry.

    Let T be a given set with n elements. Then the Steiner system S(k,m,n) is a collection S = {S1, … ,Sr} of subsets of T such that

    • |Si| = m,
    • For any subset R in T with |R| = k there is a unique i, 1<=i<=n such that R is contained in Si. |S(k,m,n)| = (n choose k)/(m choose k).

    If any set H has cardinality 6 (respectively 8, 12) then H is called a hexad, (respectively octad, dodecad.)

    Let’s look at the Steiner System S(5,6,12) and M12. We want to construct the Steiner system S(5,6,12) using the projective line P1(F11). To define the hexads in the Steiner system, denote

    • the projective line over F11 by P1(F11)={inf,0,1,…,10}.
    • Q = {0,1,3,4,5,9}=the quadratic residues union 0
    • G = PSL2(F11)
    • S = set of all images of Q under G. (Each element g in G will send Q to a subset of P1(F11). )

    There are always six elements in such a hexad. There are 132 such hexads. If I know five of the elements in a hexad of S, then the sixth element is uniquely determined. Therefore S is a Steiner system of type (5,6,12).

    Theorem:
    M12 sends a hexad in a Steiner system to another hexad in a Steiner system. In fact, the automorphism group of a Steiner system of type (5,6,12) is isomorphic to M12.

    (For a proof, see [11], Theorem 9.78.)

    The hexads of S form a Steiner system of type (5,6,12), so

    M12 = < g in S12 | g(s) belongs to S, for all s in S > .

    In other words, M12 is the subgroup stabilizing S. The hexads support the weight six words of the Golay code, defined next. (For a proof, see  [6].)

    3. Golay Code

     

    ” The Golay code is probably the most important of all codes for both practical and theoretical reasons.” ([17], pg. 64)

    M. J. E. Golay (1902-1989) was a Swiss physicist known for his work in infrared spectroscopy among other things. He was one of the founding fathers of coding theory, discovering GC24 in 1949 and GC12 in 1954.

    A code C is a vector subspace of (Fq)for some n >=1 and some prime power q =pk.
    An automorphism of C is a vector space isomorphism, f:C–>C.

    If w is a code word in Fqn, n>1, then the number of non-zero coordinates of w is called the weight w, denoted by wt(w). A cyclic code is a code which has the property that whenever (c0,c1,…,cn-1) is a code word then so is (cn-1,c0,…,cn-2).
    If c=(c0,c1,…,cn-1) is a code word in a cyclic code C then we can associate to it a polynomial g_c(x)=c0 + c1x + … + cn-1xn-1. It turns out that there is a unique monic polynomial with coefficients in Fq

    of degree >1 which divides all such polynomials g_c(x). This polynomial is called
    a generator polynomial for C, denoted g(x).

    Let n be a positive integer relatively prime to q and let alpha be a primitive n-th root of unity. Each generator g of a cyclic code C of length n has a factorization of the form g(x) = (x-alphak1)… (x-alphakr), where {k1,…,kr} are in {0,…,n-1} [17]. The numbers alphaki, 1≤ i≤ r, are called the zeros of the code C.

    If p and n are distinct primes and p is a square mod n, then the quadratic residue code of length n over Fp is the cyclic code whose generator polynomial has zeros
    {alphak | k is a square mod n} [17]. The ternary Golary code GC11 is the quadratic
    residue code of length 11 over F3.

    The ternary Golay code GC12 is the quadratic residue code of length 12 over F3 obtained by appending onto GC11 a zero-sum check digit [12].

    Theorem:
    There is a normal subgroup N of Aut(GC12) of order 2 such that Aut(GC12)/N is isomorphic to M12. M12 is a quotient of Aut(GC12) by a subgroup or order 2. In other words, M12 fits into the following short exact sequence:

    1–>N–>Aut(GC12)–>M12–>1

    Where i is the embedding and N in Aut(GC12) is a subgroup of order 2. See [6].

    4. Hadamard Matrices

     

    Jacques Hadamard (1865-1963) was a French mathematician who did important work in analytic number theory. He also wrote a popular book “The psychology in invention in the mathematical field” (1945).

    A Hadamard matrix is any n x n matrix with a +1 or -1 in every entry such that the absolute value of the determinant is equal to nn/2.

    An example of a Hadamard matrix is the Paley-Hadamard matrix. Let p be a prime of the form 4N-1, p > 3. A Paley-Hadamard matrix has order p+1 and has only +1’s and -1’s as entries. The columns and rows are indexed as (inf,0,1,2,…,p-1). The infinity row and the infinity column are all +1’s. The zero row is -1 at the 0th column and at the columns that are quadratic non-residues mod p; the zero row is +1 elsewhere. The remaining p-1 rows are cyclic shifts of the finite part of the second row. For further details, see for example [14].

    When p = 11 this construction yields a 12×12 Hadamard matrix.

    Given two Hadamard Matrices A, B we call them left-equivalent if there is an nxn signed permutation matrix P such that PA = B.

    The set {P nxn signed permutation matrix| AP is left equivalent to A} is called the automorphism group of A. In other words, a matrix is an automorphism of the Hadamard matrix, if it is a nxn monomial matrix with entries in {0,+1,-1} and when it is multiplies the Hadamard matrix on the right, only the rows may be permuted, with a sign change in some rows allowed.

    Two nxn Hadamard matrices A, B are called equivalent if there are nxn signed permutation matrices P1, P2 such that A = P1 *B *P2.

    All 12×12 Hadamard matrices are equivalent ([13][16] pg. 24). The group of automorphisms of any 12×12 Hadamard matrix is isomorphic to the Mathieu group M12 ([14] pg 99).

     

    5. 5-Transitivity

     

    Emile Mathieu (1835-1890) was a mathematical physicist known for his solution to the vibrations of an elliptical membrane.

    The fact that M12 acts 5-transitively on a set with 12 elements is due to E. Mathieu who proved the result in 1861. (Some history may be found in [15].)

    There are only a finite number of types of 5-transitive groups, namely Sn (n>=5), An (n>=7), M12 and M24. (For a proof, see [11])

    Let G act on a set X via phi : G–>SX. G is k-transitive if for each pair of ordered k-tuples (x1, x2,…,xk), (y1,y2,…,yk), all xi and yi elements belonging to X, there exists a g in G such that yi = phi(g)(xi) for each i in {1,2,…,k}.

    M12 can also be constructed as in Rotman [11], using transitive extensions, as follows (this construction appears to be due originally to Witt). Let fa,b,c,d(x)=(ax+b)/(cx+d), let

    M10 = < fa,b,c,d, fa’,b’,c’,d’ |ad-bc is in Fqx, a’d’-b’c’ is not in Fqx >,

    q = 9.

    pi = generator of F9x, so that F9x = < pi> = C8.

    Using Thm. 9.51 from Rotman, we can create a transitive extension of M10. Let omega be a new symbol and define

    M11 = < M10, h| h = (inf, omega)(pi, pi2)(pi3,pi7) (pi5,pi6)>.

    Let P1(F9) = {inf, 0, 1, pi, pi2, … , pi7}. Then M11 is four transitive on Y0 = P1(F9) union {omega}, by Thm 9.51.

    Again using Thm. 9.51, we can create a transitive extension of M11. Let sigma be a new symbol and define

    M12 = < M11, k>, where k = (omega, sig)(pi,pi3) (pi2,pi6)(pi5,pi7). M12 is 5-transitive on Y1 = Y0 union {sig}, by Th. 9.51.

    Now that we constructed a particular group that is 5-transitive on a particular set with 12 elements, what happens if we have a group that is isomorphic to that group? Is this new group also 5-transitive?

    Let G be a subgroup of S12 isomorphic to the Mathieu group M12. Such a group was constructed in Section 1.

    Lemma: There is an action of G on the set {1,2,…,12} which is 5-transitive.

    proof: Let Sig : G –> M12 be an isomorphism. Define g(i) = Sig(g)(i), where i = {1,2,…,12}, g is in G. This is an action since Sig is an isomorphism. Sig-1(h)(i) = h(i) for all g in M12, i in Y1. Using some h in M12, any i1,…,i5

    in Y1 can be sent to any j1,…,j5 in Y1. That is, there exists an h in M12 such that h(ik) = jk, k= 1,…,5 since M12 is 5-transitive. Therefore, Sig-1(h)(ik) = jk = g(ik). This action is 5-transitive. QED

    In fact, the following uniqueness result holds.

    Theorem: If G and G’ are subgroups of S12 isomorphic to M12 then they are conjugate in S12.

    (This may be found in [7], pg 211.)

    6. Presentations

     

    The presentation of M12 will be shown later, but first I will define a presentation.

    Let G = < x1,…,xn | R1(x1,…xn) = 1, …, Rm(x1,…,xn) = 1> be the smallest group generated by x1,…,xn satisfying the relations R1,…Rm. Then we say G has presentation with generators x1,…,xn and relations R1(x1,…xn) = 1, …, Rm(x1,…,xn) = 1.

    Example: Let a = (1,2,…,n), so a is an n-cycle. Let Cn be the cyclic group, Cn = < a > =
    {1,a,…,an-1}. Then Cn has presentation < x | xn=1 > = all words in x, where x satisfies xn.=1 In fact, < x | xn = 1 > is isomorphic to < a >. Indeed, the isomorphism
    < x | xn = 1 > –> < a > is denoted by xk –> ak, 0 <= k <= n-1. Two things are needed for a presentation:

    • generators, in this case x, and
    • relations, in this case xn = 1.

    Example: Let G be a group generated by a,b with the following relations; a2 = 1, b2 = 1, (ab)2 = 1:

    G = < a,b | a2 = 1, b2 = 1, (ab)2 = 1 > = {1,a,b,ab}.

    This is a non-cyclic group of order 4.

    Two presentations of M12 are as follows:

    M12 = < A,B,C,D | A11 = B5 = C2 = D2 = (BC)2 = (BD)2 = (AC)= (AD)3 = (DCB)2 = 1, AB =A3 >

    = < A,C,D | A11 = C2 = D2 = (AC)3 = (AD)3 = (CD)10 = 1, A2(CD)2A = (CD)2 >.

    In the first presentation above, AB = B-1AB. These are found in [6] and Chap. 10 Sec. 1.6 [12].

    7. Crossing the Rubicon

     

    The Rubicon is the nick-name for the Rubik icosahedron, made by slicing the icosahedron in half for each pair of antipodal vertices. Each vertex can be rotated by 2*pi/5 radians, affecting the vertices in that half of the Rubicon, creating a shape with 12 vertices, and six slices. The Rubicon and M12 are closely related by specific moves on the Rubicon.

    Let f1, f2, …,f12 denote the basic moves of the Rubicon, or a 2*pi/5 radians turn of the sub-pentagon about each vertex. Then according to Conway,

    M12 = < x*y-1 | x,y are elements of {f1, f2, …,f12 } >.

    Actually, if a twist-untwist move, x*y-1, as above, is called a cross of the Rubicon, then M12 is generated by the crosses of the Rubicon! ([1], Chap. 11 Sec. 19 of [12])

    <a name="M12 and the Minimog”>

    8. M12 and the Minimog

     

    Using the Minimog and C4 (defined below), I want to construct the Golay code GC12.

    The tetracode C4 consists of 9 words over F3:

      0 000,     0 +++,    0 ---,         where 0=0, +=1, and -=2 all mod 3.
      + 0+-,     + +-0,    + -0+,
      - 0-+,     - +0-,    - -+0.
    

    Each (a,b,c,d) in C4 defines a linear function f : F3 –> F3, where f(x) = ax+b, f(0) = b, f(1) = f(+) = c, f(2) = f(-) = d, and a is the “slope” of f. This implies a + b = c (mod 3), b – a = d (mod 3).

    Minimog: A 4×3 array whose rows are labeled 0,+,-, that construct the Golay code in such a way that both signed and unsigned hexads are easily recognized.

    A col is a word of length 12, weight 3 with a “+” in all entries of any one column and a “0” everywhere else. A tet is a word of length 12, weight 4 who has “+” entries in a pattern such that the row names form a tetracode word, and “0” entires elsewhere. For example,

     
                 _________          _________
                 | |+    |          | |+    |
                 | |+    |          |+|  +  |
                 | |+    |          | |    +| 
                 ---------          ---------               
                 "col"              "tet"
    

    The above “col” has “+” entries in all entries of column 2, and “0” entries elsewhere.
    The above “tet” has a “+” entry in each column. The row names of each “+” entry are +, 1, +, – respectively. When put together, + 0+- is one of the nine tetracode words.

    Lemma: Each word belongs to the ternary Golay Code GC12 if and only if

    • sum of each column = -(sum row0)
    • row+ – row is one of the tetracode words.

    This may be found in [4].

    Example:

    |+|+ + +|      col sums: ----      row+ - row-: --+0
    |0|0 + -|      row0 sum: + = -(sum of each col)
    |+|+ 0 -|
    
    

    How do I construct a Golay code word using cols and tets? By the Lemma above, there are four such combinations of cols and tets that are Golay code words. These are: col – col, col + tet, tet – tet, col + col – tet.

    Example:

      col-col         col+tet      tet-tet       col+col-tet
    
     | |+   -|       | |+ +  |    |+|0 + +|      | |- + +|
     | |+   -|       |+|  -  |    |-|  -  |      |-|  0 +|
     | |+   -|       | |  + +|    | |    -|      | |  + 0| 
      ? ? ? ?         + 0 ? -      - ? - +        + 0 + -
    

    “Odd-Man-Out”: The rows are labeled 0,+,-, resp.. If there is only one entry in a column then the label of the corresponding row is the Odd Man Out. (The name of the odd man out is that of the corresponding row.) If there is no entry or more than one entry in the column then the odd man out is denoted by “?”.

    For example, in the arrays above, the Odd-Men-Out are written below the individual arrays.

    For the Steiner system S(5,6,12), the minimog is labeled as such:

                                  ______________
                                  |0  3 inf  2 |
                                  |5  9  8  10 |
                                  |4  1  6  7  |
                                  --------------
    

    The four combinations of cols and tets above that construct a Golay code word yield all signed hexads. From these signed hexads, if you ignore the sign, there are 132 hexads of the Steiner system S(5,6,12) using the (o, inf, 1) labeling discussed in Section 9 below. There are a total of 265 words of this form, but there are 729 Golay code words total. So, although the above combinations yield all signed hexads, they do not yield all hexads of the Golay code ([12] pg. 321).

    The hexad for the tet-tet according to the S(5,6,12) Minimog above would be (0, inf, 2, 5, 8, 7).

    The rules to obtain each hexad in this Steiner system is discussed in Section 9 below.

    A Steiner system of type (5,6,12) and the Conway-Curtis notation can be obtained from the Minimog. S12 sends the 3×4 minimog array to another 3×4 array. The group M12 is a subgroup of S12 which sends the Minimog array to another array also yielding S(5,6,12) in Conway-Curtis notation.

    9. Kitten

     

    The kitten is also an interesting facet of the Minimog. Created by R.T. Curtis,
    kittens come from the construction of the Miracle Octal Generator, or MOG, also created by R.T. Curtis. (A description of the MOG would be too far afield for this post, but further information on the MOG can be gotten from [3] or [6].)

    Suppose we want to construct a Steiner system from the set T = {0, 1, …, 10, inf}.
    The kitten places 0, 1, and inf at the corners of a triangle, and then creates a rotational symmetry of triples inside the triangle according to R(y) = 1/(1-y) (as in [2], section 3.1). A kitten looks like:

                                    infinity
    
                                       6
    
                                    2     10
    
                                 5     7      3
    
                              6     9      4     6
    
                           2    10     8      2     10
    
                     0                                    1
    
                                Curtis' kitten               
    

    where 0, 1, inf are the points at infinity.

    Another kitten, used to construct a Steiner system from the set T = {0, 1, …, 10, 11} is

                                       6
    
                                       9
    
                                    10     8
    
                                 7     2      5
    
                              9     4     11     9
    
                          10     8     3      10     8
    
                     1                                    0
    
                             Conway-Curtis' kitten
    

    The corresponding minimog is

                      _________________________
                      |  6  |  3  |  0  |  9  |
                      |-----|-----|-----|-----|
                      |  5  |  2  |  7  | 10  |
                      |-----|-----|-----|-----|
                      |  4  |  1  |  8  | 11  |
                      |_____|_____|_____|_____|
    

    (see Conway [3]).

    The first kitten shown consists of the three points at 0, inf, 1 with an arrangement of points of the plane corresponding to each of them. This correspondence is:

             6 |10 | 3              5 | 7 |3               5 | 7 | 3 
             2 | 7 | 4              6 | 9 |4               9 | 4 | 6 
             5 | 9 | 8              2 |10 |8               8 | 2 |10
     
            inf-picture             0-picture              1-picture
    

    A union of two perpendicular lines is called a cross. There are 18 crosses of the kitten:

                    ___________________________________________
                    |* * * |* * * |* * * |*     |  *   |    * |
                    |*     |  *   |    * |* * * |* * * |* * * |
                    |*     |  *   |    * |*     |  *   |    * |
                     -----------------------------------------
                     _________________________________________
                    |*     |  *   |* *   |*     |*   * |    * |
                    |*     |  *   |* *   |  * * |  *   |    * |
                    |* * * |* * * |    * |  * * |*   * |* * * |
                     -----------------------------------------
                     _________________________________________
                    |*   * |    * |  * * |  *   |  * * |* *   |
                    |*   * |* *   |*     |*   * |  * * |    * |
                    |  *   |* *   |  * * |*   * |*     |* *   |
                    ------------------------------------------
    
    

    A square is a complement of a cross. The 18 squares of a kitten are:

                    ___________________________________________
                    |      |      |      |  * * |*   * |* *  |
                    |  * * |*   * |* *   |      |      |     |
                    |  * * |*   * |* *   |* *   |*   * |* *  |
                     -----------------------------------------
                     _________________________________________
                    |  * * |*   * |    * |  * * |  *   |* *   |
                    |  * * |*   * |    * |*     | *  * |* *   |
                    |      |      |* *   |*     |  *   |      |
                     -----------------------------------------
                     _________________________________________
                    |  *   |* *   |*     |*   * |*     |    * |
                    |  *   |    * |  * * |  *   |*     |* *   |
                    |*   * |    * |*     |  *   |  * * |    * |
                     -----------------------------------------
    

    The rules to obtain a hexad in the {0,1,inf} notation are the following:

    • A union of parallel lines in any picture,
    • {0, 1, inf} union any line,
    • {Two points at infinity} union {square in a picture corresponding to omitted point at infinity},
    • {One point at infinity} union {cross in the corresponding picture at infinity}.

    (See [2])

    M12 is isomorphic to the group of automorphisms of the Steiner system S(5,6,12) in the Conway-Curtis notation.

    10. Mathematical Blackjack or Mathieu’s 21

    Mathematical Blackjack is a card game where six cards from the group {0,1,…,11} are laid out face up on a table. The rules are:

    • each player must swap a card with a card from the remaining six, that is lower than the card on the table;
    • the first player to make the sum of all six cards less than 21 loses.

    According to Conway and Ryba [8, section V, part (d)], the winning strategy of this game is to choose a move which leaves a Steiner hexad from S(5,6,12) in the shuffle
    notation, whose sum is greater than or equal to 21, on the table.

    The shuffle notation for the hexad, used in the Mathematical Blackjack game, is shown below (see also the description in the hexad/blackjack page):

                  8 |10 |3            5 |11 |3            5 |11 |3
                  9 |11 |4            2 | 4 |8            8 | 2 |4 
                  5 | 2 |7            7 | 9 |10           9 |10 |7 
             
                 0-picture          1-picture          6-picture
    

    Riddle: Assuming the strategy, player A just made a winning hexad move that will force player B to make the sum under 21 on his next turn. Joe Smith walks up to player B and offers to shuffle all 12 cards while player A isn’t looking, for a fee. Player B grabs at his chance thinking that a random shuffle will let him back in the game. How is it that player B still loses?

    Joe is actually working for Player A. Joe does not shuffle the cards randomly, but instead uses the M12 group generated by r, s (see section 1) to shuffle the cards. Since the M12 group preserves hexads, player A still has a winning game. (He and Joe split the money.)

    11. Sporadic Groups

     

    A simple group is a group with no normal subgroups except itself and {1}. Most simple groups are from a family such as PSL2(Fp), p>3 or An, n >= 5. However there exist some simple groups outside of such well known families. These are called sporadic simple groups. M12 is a sporadic simple group of order 95,040. The only smaller sporadic group is M11 of order 7,920. (See [10] pg. 211)

    <a name="Stabilizer in M24 of a dodecad”>

    12. Stabilizer in M24 of a dodecad.

     

    M24 is a sporadic simple group of order 244,823,040 containing M12 as a subgroup. The Steiner system S(5,8,24) is a collection of 8 element subsets, called octads, from a 24 element set X, with the property that any five elements in X determine a unique octad in the system. There are (24 choose 5)/(8 choose 5) = 759 of these octads. M24 is the subgroup of SX which sends the set of octads to itself. Two octads, O1, O2, intersect in a subset of X of order 0,2,4,6 or 8 [14]. If |O1 intersect O2| = 2 then O1 + O2 is order 12. Such a subset of X is called a dodecad. M12 is isomorphic to

    {g in M24 | g(O1 + O2) = (O1 + O2)} = the stablizer of the dodecad O1 + O2.
    (See [6] for details)

    Much more information can be received from the references below or from the hexad/blackjack page.

    References

     

    1. W. D. Joyner, Mathematics of the Rubik’s Cube (USNA Course notes), 1997.
    2. R. T. Curtis, “The Steiner System S(5,6,12), the Mathieu Group M12 and the ‘Kitten’ ,” Computational Group Theory, Academic Press, London, 1984.
    3. J. H. Conway, “hexacode and Tetracode- MOG and MINIMOG,” Computational Group Theory (ed. Atkinson), Academic Press, London, 1984.
    4. Vera Pless, “Decoding the Golay Code,” Transactions of Information Theory, IEEE, 1986, (pgs 561-567).
    5. R. T. Curtis, “A new Combinatorial approach to M24“, Mathematical Proceeding of the Cambridge Philosophical Society, Vol. 79, 1974.
    6. J. H. Conway, R. T. Curtis, S. P. Norton, R. A. Parker, R. A. Wilson, “M12,”,
      Atlas of Finite Groups, Clarendon Press, Oxford, 1985.
    7. Robinson, A Course in the Theory of Groups, Springer, 1996.
    8. J. H. Conway, N. Sloane, “Lexicographic Codes: Error-Correcting Codes
      from Game Theory,” Transactions on Information Theory, IEEE, 1986.
    9. A .Adler, “The modular Curve X(11) and the Mathieu group M11“,
      Proc. London Math Society 74(1997)1-28.
      See also the paper X(11) and M11.
    10. T. Thompson, From Error-Correcting Codes Through Sphere
      Packings to Simple Groups
      , The Mathematical Association of
      America, 1983.
    11. Rotman, J, Introduction to the Theory of Groups, 4th ed.
      Springer-Verlag, 1995.
    12. J. Conway, N. Sloane, Sphere Packings, Lattices, and Groups,
      Springer-Verlag, 3rd ed., 1999.
    13. B. Kostant, “The Graph of the truncated icosahedron and the
      last letter of Galois.” Notices of the A.M.S. 42(1995)959-
      968.
    14. E. Assmus, “On the Automorphism Groups of Paley-Hadamard
      Matrices.” Combinatorial Mathematics and its Applications.
      University of North Carolina Press, 1969, (pgs 98-103).
    15. P. Greenberg, Mathieu Groups, Courant Institute of Math and
      Science, New York University, 1973.
    16. P. Cameron, J. Van Lint, Designs, Graphs, Codes, and Their
      Links
      , London Mathematical Society, Cambridge University
      Press, 1991.
    17. F. MacWilliams, N. Sloane, The Theory of Error Correcting
      Codes
      , North Holland Publishing Company, 1978.
    18. R. Graham, P. Diaconis, W. Kantor, “The Mathematics of
      Perfect Shuffles”, Advanced Applied Math, Vol. 4, 1985, (pgs
      175-196).

    Typed into html by wdj, 4-18-97.
    Corrections 4-27-2001.
    Last updated 2018-06-10.

     

Jesse Douglas – yet another Beautiful Mind?

Nobel prize winner John Nash struggled with mental illness for most of his life. His struggles were described Sylvia Nasar’s well-known biography, A Beautiful Mind, as well as a film of the same name starring Russell Crowe. Now, John Nash is practically a household name, honored for his contributions to the mathematics of game theory and economics.

While Jesse Douglas isn’t nearly as well-known as Nash, and didn’t win a Nobel Prize, he was (with Lars Ahlfors in 1936) the first recipient of the Fields Medal. According to one source, he too struggled with mental illness. However, before talking about the life of Jesse Douglas, let’s talk about his mathematical research.

Jesse-Douglas-1940-1941_250x250

Jesse Douglas in his 40s.

His Mathematics

Jesse Douglas solved a geometrical problem formulated in the 1700’s now called Plateau’s problem.  Plateau’s problem (also known as the soap bubble problem) is to show the existence of a minimal surface with a given boundary. Plateau was a a Belgian physicist who lived in the 1800s, known for his experiments with soap films. A minimal surface is a surface that locally minimizes its area. For example, the catenoid (see below) obviously doesn’t have minimum area for its (disconnected) boundary, but any piece of it bounded by a (connected) closed curve does have minimum area.

 

 

 

His research was not universally recognized at first. According to Reid [R76]:

Arriving in the year 192901930 at the end of a European tour as a National Research Fellow, he proposed to talk about his not-yet-published work on Plateau’s Problem at the weekly meeting of the mathematische Gesellschaft [at Gottingen University]. The problem had been around for a long time. Many outstanding mathematicians, including Riemann himself, had worked on it. The members of the Gesellschaft simply did not believe that an American had solved it. … When he had finished his presentation, some of the members of the Gesellschaft took him severely to task on almost every detail of the proof. Let left Gottingen deeply offended …

Douglas solved this geometrical problem in his paper “Solution of the problem of Plateau,” Trans. Amer. Math. Soc. 33 (1931)263–321. Gray and Micallef [GM07] do an excellent job of describing Jesse’s mathematical approach to the problem. Jesse continued working on generalizing and refining aspects of his research on this problem for the next 10 years or so. Some of his papers in this field include

  • A method of numerical solution of the problem of Plateau, Annals of Mathematics 29(1927 – 1928)180-188
  • Solution of the problem of Plateau, Trans. Amer. Math. Soc. 33 (1931) 263–321.
  • Systems of K-dimensional manifolds in an N-dimensional space, Mathematische Annalen 105 (1931) 707-733.
  • The Problem of Plateau for Two Contours, Studies in Applied Mathematics 10(1931)315-359.
  • One-sided minimal surfaces with a given boundary, Trans. Am Math Soc 34(1932)731–756.
  • A Jordan space curve no arc of which can form part of a contour which bounds a finite area, Annals of Mathematics 35(1934) 100-103.
  • Green’s function and the problem of Plateau, American Journal of Mathematics. 61 (1939) 545–589.
  • The most general form of the problem of Plateau, American Journal of Mathematics. 61 (1939)590–608.
  • Solution of the inverse problem of the calculus of variations, Proceedings of the National Academy of Sciences. 25(1939) 631–637.
  • The analytic prolongation of a minimal surface across a straight line, Proc Natl Acad Sci USA. 25(1939)375-377.
  • The higher topological form of Plateau’s problem, Annali della Scuola Normale Superiore di Pisa , 8 (1939)195-218.
  • Minimal surfaces of higher topological structure, Annals of Mathematics 40(1939) 205-298 .
  • Theorems in the inverse problem of the calculus of variations, Proc Natl Acad Sci USA, 26(1940) 215–221.

He also wrote papers in other fields in mathematics, for example, the theory of finite groups. For example, in 1951 he published a series of short articles On finite groups with two independent generators, all in the Proc. Natl. Acad. Sci. USA. Also, in 1953, he published a series of short articles On the basis theorem for finite abelian groups, again, all in the Proc. Natl. Acad. Sci. USA.

His Life

Jesse’s father, Louis, was a Polish immigrant who moved to Canada sometime in the late 1800s. The family name was changed at Canadian Immigration and I don’t know his original name. At some point, Louis married Sarah Kommel (I don’t know when the move occurred, nor when and where they met) and moved to New York City. I think both Louis and Sarah were Jewish. Jesse was born in NYC on July 3rd, 1897. According to a geneology website, Sarah’s parents were Leazar Louis Kommel (1851-1923) and Chaya Lande, who died in 1906. Jesse’s mother Sarah passed away in 1939, a year before Jesse married (as we will see below).

Jesse was educated at public schools in New York City. According to Steinhardt [St92], Jesse had a photographic memory. After graduation from high school, he entered the City College of New York and won the Belden Medal for excellence in mathematics in his first year at City College of New York, the youngest recipient at the time. He graduated with a B.Sc. cum laude in February 1916 at the age of 18. Then he entered Columbia University to undertake research under the supervision of Edward Kasner. He took part in Kasner’s seminar on differential geometry and it was there that Jesse first heard of Plateau’s Problem. He submitted his doctoral thesis On Certain Two-Point Properties of General Families of Curves in 1920 (some references say 1921). After getting his PhD, he was an instructor at Columbia from 1920 to 1926. (Was this in the statistics department or the mathematics department? I don’t know.)

He won a National Research Fellowship and as a result, traveled widely for four years, visiting Princeton and Harvard in 1927, Chicago in 1928, and Paris from 1928 to 1930, with trips to Gottingen, Hamburg, and Rome. Jesse was offered an Assistant Professor position at MIT in January 1930. He took leave of absence for a term in 1932. After his promotion to Associate Professor at MIT in 1934, he almost at once took leave of absence again to be a Research Worker at the Institute for Advanced Study in Princeton from 1934 to 1935.  After returning to MIT, Jesse was awarded the first Fields Medal, together with Lars Ahlfors, in 1936. Once again, Jesse took leave of absence from MIT in 1936, and on 1 July 1938, he resigned. According to Aronson [A13], Jesse “became ill” at this time and Levinson was eventually hired as his replacement.

This may seem like the romantic life of a world-class mathematician, but it really is very unusual to be “homeless” 18 years after your get your PhD degree. Something is going on. According to Hersh [H10]:

Douglas’s name is almost forgotten today. He is a rather tragic figure, one of several important mathematicians gravely handicapped by what are now called bipolar, and used to be called manic-depressive, symptoms. He had a junior position at M.I.T., which he lost as a result of inability to perform consistently in the classroom.

According to Steinhardt [St92],

… the volatility of this mix of great gifts and sharp emotions [in which his “over-sized sensitivity surfaced not infrequently in intense feelings articulated vigorously to colleagues”] in Douglas’ makeup worked to the disadvantage of the great American mathematician’s external fortunes.

I don’t know what he did professionally in the 1939-1940 period, but he married Jessie Nayler on June 30th, 1940 (they had one son Lewis Philip Douglas). The husband named Jesse and the wife named Jessie! He often taught summer courses at Columbia University, as well as courses at Yeshiva University. The following year, Jesse was a Guggenheim fellow at Columbia University from 1940 to 1941. He taught at Brooklyn College from 1942 to 1946, as an assistant professor. His teaching during these war years went well according to a letter of commendation (did he teach students from the US Military Academy during this time?), and he received a Distinguished Service Award. In 1945, Jesse wrote an application letter for a teaching position saying [St92]:

This semester I have a teaching load of 19 hours per week at Brooklyn College, including two advances courses: complex variables and calculus of variations.

It is no surprise that he published nothing in the period 1942-1950. According to Micallef [M13],

There is a blank in Douglas’s career from 1946 to 1950, and the later part of Douglas’ life seems to have been troubled. His marriage ended in divorce in 1950.

In 1951, Jesse published a string of papers in a completely different field, finite group theory. Perhaps the end of Jesse’s marriage to Jessie caused him to reinvent himself, mathematically?

According to some sources, Douglas harbored resentment towards his research “competition” Rado (who he strongly beieved should not be credited with solving Plateau’s Problem [St92]) and Courant, as well as J.F. Ritt at Columbia, who may have blocked his appointment to a permanent position at his alma mater. (Sadly, anti-Semitic prejudices common of those times may have played a role in Jesse’s troubles in this regard.) Never-the-less, he is remembered as a helpful, sympathetic, and approachable colleague [St92].

His resentment of Courant was unfortunate because some 10 years earlier, in March 1935, Courant invited Jesse to speak at NYU. According to Reid [R76]:

Courant’s invitation to Douglas to speak at NYU was to a certain extent an olive branch. … Remembering this past history [Jesse’s reception at Gottingen 5 years earlier], Courant did his best to make Douglas’ lecture at NYU an event.

Clearly, Courant appreciated Jesse’s work and tried to treat him with due respect.

Other than Jesse’s mathematical research, this does not appear to have been a happy period in Jesse’s life. According to O’Conner and Robertson [OCR],

His [ex-]wife, Jessie Douglas died in 1955, the year in which Douglas was appointed professor of mathematics at the City College of New York. He remained in that post for the final ten years of his life, living in Butler Hall, 88 Morningside Drive in New York.

Steinhardt [St92] recalls the difficulty that Abraham Schwartz had in having CCNY give tenure at the full professor level to Jesse, when he was 1958. Except for the logician Emil Post, there was no one at CCNY at the time close to Jesse’s stature as a researcher. Ironically, a number of CCNY mathematicians “felt threatened by the appointment”, presumably fearing Jesse would require them to work harder.

There are a number of anecdotes of Jesse Douglas and his interactions with students. They aren’t universally flattering and I hesitate to repeat the negative ones, as I don’t know how typical they are. A number of sources suggest that when he was feeling well, he could be an impressive, charismatic, self-confident expositor and a lucid  teacher. While I don’t know the teaching load at Columbia University the time (where he belonged), I do know that, compared to today’s teaching loads, they were often quite heavy (see for example Parikh’s biography, The Unreal Life of Oscar Zariski). Are these unflattering reports of Jesse’s interactions with students merely due to his oppressive teaching load and inability to handle that volume of students? I don’t know. Perhaps he held others to the same very high standards that he held himself in his own mathematical research.

Unfortunately, not enough is known about this very remarkable mathematician.

References

[A13] D.G. Aronson, Norman Levinson: 1912-1975, Biographical memoirs, Nat. Acad. Sci., 2013.

[GM07] Jeremy Gray, Mario Micallef, The work of Jesse Douglas on minimal surfaces, 2013. (appeared in Bull AMS 2008)  pdf

[H10] R. Hersh, Under-represented then over-represented, College J. Math, 2010.

[M13] M. Micallef, The work of Jesse Douglas on Minimal Surfaces, talk at Universidad de Granada, 2013-02-07.

[OCR] John J. O’Connor and Edmund F. Robertson, Jesse Douglas.

[R76] C. Reid, Courant, Springer-Verlag, 1976.

[St92] F. Steinhardt, “Jesse Douglas as teacher and colleague”, in The Problem of Plateau, ed. T. Rassias, World Scientific, 1992.