Category Archives: teaching mathematics

The mathematician and the Pope

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Acknowledgement: This could not have been written without the helpful conversations and correspondences with these brilliant scholars: Edray Goins (Pomoma), John Stigall (Howard), Nathan Alexander (Howard), and Susan Kelly (Univ Wisconsin, retired).  Also, I thank the librarians at the Catholic University of America for their help. I’m deeply indebted to them for sharing their knowledge about Haynes’ life and work and philosophy, but if there are mistakes, and I’m sure there are, they are my responsibility alone.

This is a non-technical (I promise!) introduction to the life and work of Euphemia Haynes. She’s a fascinating character known not just for her pure mathematics PhD thesis earned at the age of 53, but for her extraordinary devotion to improving mathematics education for everyone, as well as her service to many charities, especially those related to the Catholic Church.

At the time, the prestigious Papal Decoration of Honor medal, the Pro Ecclesia et Pontifice, was the top award for non-clergy (in particular, all women) bestowed by the Pope. That papal recognition was given to Haynes by Pope John XXIII, when she was almost 70. In fact, hers was the only Pro Ecclesia et Pontifice medal bestowed by the Pope to anyone during his entire tenure.

This is an introduction to her life’s journey.

Upbringing

Born Martha Euphemia Lofton in Washington D.C. on September 11, 1890, Euphemia preferred using her middle name.

Her father, Dr William Lofton, was a dentist while her mother Lavinia was very active in her church and later became an elementary school teacher in the D.C. school district. According to saved correspondence, the family lived on 17th Street and attended, until Euphemia was in her 20s, St Augustine’s which was a few blocks away. Lavinia and Euphemia and Joseph were part of the church choir for many years. Indeed, Lavinia was the organist for the junior choir since Euphemia was a baby.

This familial foundation within the Catholic community likely instilled in Euphemia her values of service, justice, and community engagement.

Education

Lavinia Lofton started teaching in the DC public schools in the fall of 1901 (when Euphemia was 11, as a kindergarten assistant. She was permanently appointed teacher in the DC school system a few years later in the summer of 1903.

Inspired no doubt by her mother, Euphemia’s educational journey was marked by consistent excellence. In fact, she distinguished herself early, graduating as valedictorian from M Street High School in 1907.

Euphemia Lofton began her own teaching career in the DC elementary school system in the fall of 1909. She taught there until the summer of 1912. Then she left for Smith College, where she earned an undergraduate mathematics major (and psychology minor) in 1914. During this time, the letters from her fiance Harold Haynes discuss his plans to visit her, as well as keeping her up to date on various family and business matters in DC. Upon graduation, she immediately returned to DC and started teaching in the high school system in the fall of 1914.

She taught  in the Miner Normal School and various local high schools, such as Dunbar, until 1930. At that point she began teaching at the Miner Teachers College (later assimilated with others to become the University of the District of Columbia). As an early indication of her extraordinary administrative talents, Euphemia quickly founded and established the mathematics department at Miner as well.

Marriage

In 1917 she married Harold Appo Haynes, a teacher like herself. The couple had no children. He was a childhood friend and, based on saved letters between he and Euphemia, a source of constant encouragement and strength for her. Harold had a EE degree from the University of Pennsylvania in 1910, and later earned a masters in education from the University of Chicago in 1930, and a doctorate in education from New York University in 1946.

Besides Euphemia’s promotion to teach at the college level, another significant event occurred in 1930. In that year, Euphemia obtained a Master’s degree in education from the University of Chicago (with her husband Harold).

Master’s thesis

Euphemia’s thesis was a significant piece of scholarship. In it, she discussed test validity and student assessment methods. With the goal to trace the evolution of testing in elementary and secondary school mathematics from 1900 to 1930, she focused on the main mathematics subjects arithmetic, algebra, and plane geometry. She surveyed published education literature, analyzed actual test instruments in those subjects, and summarized reports by educators and administrators on their own test development.

In the early 1900s, teachers gave a large group of students the same problem and compared how they did, without a grading key or separation into different skill metrics. In her thesis she notes that educators since those early 1900s started moving away from subjective grading to data-driven evaluation. Tests need to focus objectively on specific abilities. Tests moved from general surveys to fine-grained skill analysis. Indeed, by the 1910s–1920s, grade school tests measured discrete skills within core mathematics subjects. Testing evolved as a tool for diagnosis, curriculum evaluation, and teacher development. She also notes the progression toward standardized tests and its use for diagnosing student challenges.

In fact, after retiring from teaching she because president of the DC Board of Education (the first woman to hold that office). By that point she had grown to be an extremely gifted administrator with a single-minded focus on service to both education and her church. The timing of her presidency, following the 1954 Brown v. Board decision and amidst ongoing civil rights litigation,positioned her to directly implement and enforce desegregation and equity policies. Indeed, it was during her term as president that the track system was eliminated. (For further details, see the discussion of the 1967 Hobson v Hansen District Court case in [KSZ14]).

Volunteer service

Here we mention just a few of the many volunteer efforts Euphemia Haynes generously devoted her time to.

A common thread throughout her life was her commitment to social justice and her leadership in various Catholic organizations.

She co-founded the Catholic Interracial Council of the District of Columbia, an organization she helped establish to promote racial harmony and justice within the Church and broader community. These Catholic Interracial Councils, which united under a national umbrella in 1959-1960, were founded with the explicit aim of bridging racial divides between White and Black populations. The D.C. Council, for instance, played a vital role in coordinating Catholic participation in significant civil rights events.

Haynes also held a significant leadership position as the first vice president of the Archdiocesan Council of Catholic Women, which focused on education, social justice, and assisting immigrants. She later served as president of the local chapter of that Council.

After retiring from teaching in 1959, she didn’t stop working but served others through organizations such as  

  • the AAUW (American Association of University Women),
  • the Committee of International Social Welfare,
  • the Executive Committee of the National Social Welfare Assembly,
  • the D.C. Health and Welfare Council,
  • the USO,
  • the Urban League,
  • the NAACP, and
  • the League of Women Voters,

to name a few. Her unwavering commitment to social justice was fueled by the Catholic moral imperative to combat racism.

Her involvement with the Catholic Interracial Council and her broader civil rights work exemplify how Catholic social teaching provided a moral framework for her and other social activists during the Civil Rights era. The Church’s evolving stance on racial justice provided a powerful moral authority and a network (such as the Catholic Interracial Council and the Archdiocesan Council of Catholic Women) through which individuals like Haynes could actively pursue desegregation and equality. Her life demonstrates the practical application of religious principles to pressing social issues, showcasing how faith communities were critical sites for civil rights organizing and advocacy.

PhD thesis

Haynes earned her PhD from Catholic University of America (CUA) in 1943. The thesis topic itself is quite technical, to say nothing of the methods and proofs in the thesis itself. Just to understand the setup for the problem she solved in her thesis requires, at a minimum, knowing differential calculus.

In essence, Haynes’ thesis delves into the core of enumerative geometry, a field concerned with counting geometric objects satisfying specific conditions. For example, since the time of the ancient Greeks, mathematicians have known of the Problem of Apollonius: what is the number of circles tangent to three circles in general position? (It turns out the answer is 8.) Euphemia’s problem was in a similar spirit but was of course much harder and, as pointed out, even quite technical to state. None-the-less, her work is rooted in “synthetic” methods from the 1800s emphasizing geometric constructions and reasoning without explicit reliance on algebraic calculations. One of her innovations was to make extensive use of those algebraic calculations that the synthetic method was designed to avoid! In some sense, she solved her thesis problem by first reformulating it in a more complicated mathematical framework, then she worked out the solution.

It may be worth noting that after the 1940s, the methods she used were virtually abandoned for a direct algebraic approach, using machinery borrowed from the relatively recent methods of commutative algebra. For a few more technical details are her thesis see my earlier post “Remarks on …“, also available on this blog.

By the way, she was the first Black woman to ever earn a PhD in mathematics anywhere in the United States. She never requested recognition for this achievement. Indeed, it was over 50 years later when historians of mathematics recognized it was actually her and not someone else!

Unassuming but determined, Euphemia immediately put her understanding of advanced mathematics to work, inspiring not only students in the classroom, but also teachers. In a 1945 address to a meeting of DC mathematics teachers, Euphemia spoke about the unifying nature of what she called symbolic logic. Just as the physicist studies the natural world using rocks, plants, and other physical materials, the mathematician works within the world of logic. She explained that, instead of stones or chemicals, the ”tools” of the mathematician are facts, ideas, relationships, and implications. The abstract objects of logic are the raw materials of advanced mathematics, shaping the universe in which mathematicians explore and create.

Boiled down to its essence, her message to the mathematics teachers in the audience was: Through your service, you are teaching your students to better understand the world around them.

She achieved many academic awards in her life for her service. Another kind of award was bestowed on her from the Catholic Church.

The Pro Ecclesia et Pontifice medal

Just shy of 70 years old, in 1959 Euphemia Lofton Haynes was awarded the Papal Medal, Pro Ecclesia et Pontifice, by Pope John XXIII. This recognized her “outstanding valour and bravery on behalf of the Church and Society,” as well as her extraordinary services to the Church while maintaining fidelity to God and to the Pope. This Papal Decoration of Honor was a powerful affirmation of Euphemia Haynes’s entire life — her academic, professional, and civil rights endeavors. They were expressions of her deep faith and unwavering service to humanity. It’s an appreciation from the Catholic Church for her professional accomplishments in education, including founding departments and teaching for decades, her direct, hands-on service to the community, especially to Catholic students and teachers.

Her life lesson

Euphemia Haynes showed us that mathematics is not just about numbers. For her, it’s also about perseverance, leadership, and service. She broke barriers in higher education, stood firm in her beliefs, and used her talents to uplift others. Her life is a reminder that your passion — whether in math or something else — can connect to something larger than yourself. You don’t have to choose between mathematical expertise and helping others. As Euphemia Haynes showed us, you can do both.

References

[Ha30] E. L. Haynes, The Historical Development of Tests in Elementary and Secondary Mathematics, Masters Thesis, University of Chicago, 1930. pdf: click here

[Ha43] ——, Determination of Sets of Independent Conditions Characterizing Certain Special Cases of Symmetric Correspondences, Doctoral Thesis (advisor Aubrey Landry), The Catholic University of America, Washington DC, 1943. pdf: click here

[Ha45] —-, Mathematics – symbolic logics (typewritten and hand-written notes for a talk on the nature of advanced mathematics), address to Teachers of Mathematics in Jr. and Sr. High Schools (1945), Washington DC. (Available from the collected works of Euphemia Haynes at Catholic University of America.)

[KSZ14] S. Kelly, C. Shimmers, K. Zoroufy, Euphemia Lofton Haynes: Bringing education closer to the “goal of Perfection’‘, available online at the url arxiv.org/abs/1703.00944.

Mathematics PhD students of Aubrey Edward Landry 

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Based on information primarily sourced from the Mathematics Genealogy Project and university records, here are the Ph.D. students in Mathematics who graduated from The Catholic University of America between 1910 and 1950 under the advisement of Professor Aubrey Edward Landry:

1. Sister Mary Gervase Kelley (1917)*
Thesis Title: On the Cardioids Fulfilling Certain Assigned Conditions
2. Joseph Nelson Rice (1917)*
Thesis Title: On the In-and-Circumscribed Triangles of the Plane Rational Quartic Curve
3. Louis Antoine De Cleene (1927)*
Thesis Title: On Triangles Circumscribed about a Conic and Inscribed in a Cubic Curve
4. Frank Engelbert Smith (1928)*
Thesis Title: The Triangles In and-Circumscribed to the Triangular-Symmetric Rational Quartic
5. James Norman Eastham (1931)*
Thesis Title: The Triangles In-and-circumscribed to the Tacnodal Rational Quartic Curve with Residual Crunode
6. Sister Marie Cecilia Mangold (1929)*
Thesis Title: The Loci Described by the Vertices of Singly Infinite Systems of Triangles Circumscribed about a Fixed Conic
7. Sister Leonarda Burke (1931)*
Thesis Title: On a case of the triangles in-and-circumscribed to a rational quartic curve with a line of symmetry
8. Sister Mary de Lellis Gough (1931)
Thesis Title: On the Condition for the Existence of Triangles In-and-Circumscribed to Certain Types of Rational Quartic Curve and Having a Common Side
9. Sister Charles Mary Morrison (1931)*
Thesis Title: The Triangles In-and-Circumscribed to the Biflecnodal Rational Quartic
10. Sister Mary Felice Vaudreuil (1931)*
Thesis Title: Two Correspondences Determined by the Tangents to a Rational Cuspidal Quartic with a Line of Symmetry
11. Sister Mary Domitilla Thuener (1932)*
Thesis Title: On the Number and Reality of the Self-Symmetric Quadrilaterals In-and-Circumscribed to the Triangular-Symmetric Rational Quartic
12. Sister Mary Nicholas Arnoldy (1932)*
Thesis Title: The Reality of the Double Tangents of the Rational Symmetric Quartic Curve
13. Sister Mary Helen Sullivan (1934)*
Thesis Title: The Number and Reality of the Non-Self-Symmetric Quadrilaterals In-and-Circumscribed to the Rational Unicuspidal Quartic with a Line of Symmetry
14. Sister Mary Laetitia Hill (1935)*
Thesis Title: The Number and Reality of Quadrilaterals In-and-Circumscribed to a Rational Unicuspidal Quartic with Real Tangents from the Cusp
15. Sister Mary Henrietta Reilly (1936)*
Thesis Title: Self-Symmetric Quadrilaterals In-and-Circumscribed to the Plane Rational Quartic Curve with a Line of Symmetry
16. Sister Mary Charlotte Fowler (1937)*
Thesis Title: The discriminant of the sextic of double point parameters of the plane rational quartic curve
17. Sister Catherine Francis Galvin (1938)*
Thesis Title: Two Geometrical Representations of the Symmetric Correspondence C(N,N) with Their Interrelations
18. Euphemia Lofton Haynes (1943)*
Thesis Title: Determination of Sets of Independent Conditions Characterizing Certain Special Cases of Symmetric Correspondences
19. Brother Cyprian John McNamara (1948)*
Thesis Title: The Reality of the Double Tangents of the Rational Bicircular Symmetric Quartic

Note, most of these are nuns. Other than Brother McNamara, Landry advised de Cleene (1927) who had received his Holy Orders 8 years earlier.

Landry had three male “civilian” students (Rice, 1917, Smith 1928, Eastham, 1931) and one female “civilian” student (Haynes, 1943). Rice was later hired full time at Catholic University. Eastham was soon hired to teach at Nazareth College. Haynes, 16 years later, was awarded the highest award the Pope (Pope John XXIII) could give to a woman: the Pro Ecclesia et Pontifice medal.

An asterisk next to a name means that I’ve obtain the PhD by inter-library loan (usually from CUA). If there are any additions or missing students, please let me know!

The Beautiful Math of Tangent Lines: Unveiling the Lemniscate’s Secrets

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An experimental post

I asked Gemini to create a blog post based on this plot (below) and the Python code I wrote (based on ideas explained to me by Edray Goins) that generated it. (The prompt I fed to Gemini was modified from a prompt found online for generating a blog post about food.) The response, basically unedited, is below. The topic is inspired by the PhD thesis of Euphemia Haynes (a student of Aubrey Landry, Catholic University of America, 1943). See this earlier post for more information on it.

Mathematics can be beautiful, and sometimes that beauty is hidden in plain sight. Take, for instance, this striking image:

a graceful lemniscate curve adorned with a web of tangent lines, all seemingly converging on a single point. This isn’t some artistic rendering; it’s the output of a fascinating algorithm, written in Python and SageMath, plot_lemniscate_quartic_tangents.

This algorithm doesn’t just draw a pretty picture; it solves a sophisticated geometric problem: finding all the tangent lines to a lemniscate quartic that also pass through a specific point on the curve. Let’s dissect the magic:

  1. The Lemniscate: The algorithm begins by defining the lemniscate quartic curve. This curve, shaped like a sideways figure-eight, has a unique and elegant equation: (x² + y²)² = a(x² – y²), where ‘a’ is a constant.
  2. The Target Point: We choose a specific point, P0, on the lemniscate. This is the point where all our tangent lines will magically meet.
  3. The Tangent Hunt: This is where the algorithm gets clever. It doesn’t directly solve for the tangent lines. Instead, it cleverly iterates through numerous points (P) on the lemniscate. For each point, it calculates two things:
    • The slope of the secant line connecting P0 and P.
    • The slope of the tangent line to the lemniscate at point P.
  4. The Convergence: If the slopes from step 3 are approximately equal (within a tolerance), it means the secant line through P0 and P is also a tangent line to the curve at P. This line is added to our collection of tangent lines.
  5. The Reveal: Finally, the algorithm plots the lemniscate along with all the tangent lines it discovered. The result is the captivating image we see—a testament to the hidden geometric relationships within this seemingly simple curve.

The algorithm isn’t just about finding tangents; it reveals a beautiful correspondence between points on the lemniscate and its tangent lines. It’s a visual exploration of the intricate geometry underlying algebraic curves, demonstrating how computation can unveil hidden mathematical beauty. So next time you see a seemingly simple curve, remember the rich and sometimes surprising geometry hiding within.

Remarks on the 1943 PhD  thesis of E. Haynes

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Euphemia Haynes was a trailblazer as the first African-American woman to earn a doctorate in mathematics. The Catholic University of America holds a copy of her thesis, entitled “The Determination of Sets of Independent Conditions Characterizing Certain Special Cases of Symmetric Correspondences”, in their archives. 

A short biography: Euphemia Lofton daughter of Dr William Lofton, a dentist, and Lavinia Day Lofton, a kindergarten teacher. She was the valedictorian of M Street High School in 1907 and then graduated from (what is now known as) University of the District of Columbia with distinction and a degree in education in 1909. She was a  mathematics major at Smith College, which she graduated from in 1914. She married Harold Appo Haynes, a teacher in 1917 and earned her master’s degree in education from the University of Chicago in 1930. In 1943 she was awarded her PhD from The Catholic University of America, advised by Professor Aubrey E. Landry. An excellent, more detailed biography can be found at [KSZ] (see also http://www.math.buffalo.edu/mad/PEEPS/haynes.euphemia.lofton.html).

More of her biography is given in the post The Mathematician and the Pope, also available on this blog.

In this blog post, we merely try to explain her title. What is a “symmetric correspondence”?

We refer to Dolgachev’s notes [Do14], section 5.5: A correspondence of degree d between nonsingular curves X and Y is a non-constant morphism T:X\to Y^{(d)} to the d-th symmetric product Y^{(d)} of $Y$. Its graph is denoted \Gamma_T\subset X\times Y. The projection \Gamma_T\to X is a finite map of degree $d$, while the projection \Gamma_T\to Y is a finite map of degree e, say. It defines a correspondence Y\to X^{(e)} denoted by T^{-1}, called the inverse correspondence. Its graph \Gamma_{T^{-1}}\subset Y\times X is the image of \Gamma_T under the swap X\times Y\to Y\times X. If d is the degree of T and e is the degree of T^{-1}, we say that $T$ is a correspondence of type (d, e). This correspondence is symmetric if T = T^{-1}. 

Very roughly speaking, in her thesis, Haynes looks at various special cases of curves and in these cases she derives (technically defined) conditions that characterize the types of symmetric correspondences that arise in those cases.

While a scan of her thesis is archived at Catholic University, I have typed up her thesis in latex. For a digital copy, just email me (wdjoyner@gmail.com).

References

[Do14] I. Dolgachev, Classical Algebraic Geometry: a modern view, Cambridge Univ. Press, 2012.

(at https://mathweb.ucsd.edu/~eizadi/207A-14/CAG.pdf)

[KSZ17] Susan Kelly, Carly Shinners, Katherine Zoroufy, “Euphemia Lofton Haynes: Bringing Education Closer to the “Goal of Perfection“, preprint, 2017 (available at https://arxiv.org/abs/1703.00944). A version of this paper was also published in the Notices of the American Mathematics Society.

Another mathematician visits the ballpark – OPS

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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

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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.

Left to right: Oswald Veblen, Edmund Landau, Harald Bohr. Location, photographer, date unknown.

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

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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

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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

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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

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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