Category Archives: math

The Elevator Problem

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You are on the bottom floor (floor 0, lets call it) of an apartment building with no basement. There are n elevators, which we index 1,2,…,n. Assume the elevators are on floors f1, f2, … , where fk > 0 is the floor elevator k is currently on, 1 <= k <= n. Assume you only like one of the elevators, elevator e.


The way the elevator logic works is this: When you press the elevator button, one closest to you ( = one on floor number min(fk, k>0)) is told to go to 0. If there is a tie then, of those on the same lowest floor, the elevator with the smallest index is told to go to 0.
Move: If you press the button and some other elevator than elevator e arrives, you can tell it to go to any floor you wish.
Taboo: You can press the elevator button if and only if no elevator is moving down.
Goal: You want to use elevator e (for some fixed e=1, 2, …, n).

Problem: Is there a finite sequence of moves that allows you to ride in elevator e?

My plan is to post the answer sometime later, but have fun with it!

In the works: a book “Exploring Graphs via Harmonic Morphisms”

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Caroline Melles and I have been working for some years on a 2-volume book in graph theory which investigates harmonic morphisms. These are, roughly speaking, mappings from one graph to another that preserve locally harmonic functions on these graphs. Therefore, this topic fits into the general framework of harmonic analysis on graphs.

This post only concerns the first volume. The intent here is to mention some of the types of results we obtain. Of course, by no means is it intended to be a complete description.

The second volume will be summarized in a separate post.

Graphs in our book are unweighted and, unless stated otherwise, have no loops or multiple edges. The basic idea is this: in chapter 2 we classify harmonic morphisms using a criteria expressed as a matrix identity. For various graph-theoretical constructions (such as edge deletion or join or a graph product or …) that can be performed on a given graph Gamma, we pick a graph morphism associated to the construction (such as sending a vertex in the constructed graph to the given graph). That morphism is associated to a matrix (which we called the vertex map matrix in chapter 3 of our earlier book, Adventures in Graph Theory). When this matrix satisfies the above-mentioned matrix criteria then the associated morphism is harmonic.

Chapter 1 is on Graph Morphisms.

This chapter is devoted to background on graph morphisms and some of the methods we use to study them.

  1. Roughly speaking, a morphism is a mapping between graphs that preserves incidence structure. After defining horizontal and vertical edges, vertical multiplicities, local horizontal multiplicities, it recalls well-known graph families like cycle graphs, path graphs, and complete graphs.
  2. There are a few very useful degree identities. First, there is a fundamental formula relating vertex degrees to multiplicities under morphisms. There is also a formula for the degree
    of the morphism in terms of vertical multiplicities and local horizontal multiplicities.
  3. A topic threading through the book is that of matrix-theoretic methods. This first chapter introduces vertex map matrices and edge map matrices that encode morphisms. After establishing key matrix identities and products, reviews adjacency matrices and their spectra, with detailed analysis of cycle graph eigenvalues using Chebyshev polynomials and complex roots of unity.
  4. It recalls signed and unsigned incidence matrices, with and without edge orientations, and establishes the fundamental Graph Homomorphism Identity relating incidence matrices to morphism matrices,
  5. introduces Laplacian matrices as differences of degree and adjacency matrices, connecting to the incidence matrix framework.
  6. Introduced graph blowup morphisms via a blowup construction where vertices are replaced by independent sets, creating natural homomorphisms with specific structural properties.
  7. Some functorial properties of graph morphisms are established, such as how morphisms behave under graph constructions like subdivisions, smoothing, deletions, and substitutions.
  8. The chapter ends with exercises and a chapter summary.

Chapter 2 on Harmonic Morphisms

    This chapter is devoted to the basics of harmonic morphisms.
  1. Introduces the core definition: a graph morphism is harmonic if local horizontal multiplicities are constant across edges incident to each vertex’s image.
  2. Cycle space and cocycle space – Develops the algebraic framework using homology and cohomology of graphs. Covers Urakawa’s theorem on pullbacks of harmonic 1-forms and Baker-Norin results on divisors and Jacobians.
  3. Matrix-theoretic methods – Establishes the fundamental matrix characterization: a morphism is harmonic iff there exists a diagonal multiplicity matrix satisfying specific adjacency matrix identities. Proves equivalence with an analogous Laplacian matrix identity and an analogous incidence matrix criteria.
  4. The Riemann-Hurwitz formula – Presents the graph-theoretic analogue relating genera of graphs via harmonic morphisms, with matrix proof and applications to regular graphs.
  5. Some functorial consequences – Demonstrates how harmonic morphisms interact with graph constructions like subdivision, edge substitution, leaf addition, and deletion. Shows these
    operations preserve harmonicity under appropriate conditions.
  6. The chapter ends with exercises and a chapter summary.

All harmonic morphisms from this graph to C4 are covers.

  1. Fundamental Problem: Given a graph Gamma1, for which graphs Gamma2 is there a non-trivial harmonic morphism phi from Gamma2 to Gamma1?
  1. Follow-up question: Can the number of such phi be counted?

Chapter 3 on Counting Problems

This chapter looks at various families, such as the path graphs. What is especially remarkable is that, as we will see, the problem of counting harmonic morphisms often boils down to solving certain recurrance relations, some of which arose (in a completely different context of course) in
the work of medieval mathematicians, both in Europe and in India.

  1. Regarding harmonic morphisms between path graphs, we show how to construct and count the harmonic morphisms from longer path graphs to shorter ones.
  2. Regarding harmonic morphisms between cycle graphs, we show how to construct and count the harmonic morphisms from larger cycle graphs (when they exist) to smaller ones. It turns out all such harmonic morphisms are necessarily covers.
  3. Regarding harmonic morphisms between complete graphs, we show how to construct and count the harmonic morphisms from larger complete graphs (when they exist) to smaller ones.
  4. Harmonic morphisms to P2 (arising from the Baker-Norin Theorem) can be counted.
  5. Harmonic morphisms to P3 (the path graph with only 3 vertices) can be counted in special cases.
    There are lots of open questions, such as which trees have a harmonic morphism to P3.
  6. The chapter ends with exercises and a chapter summary.

Chapter 4 on Harmonic Quotient Morphisms

    This chapter studies quotient graphs arising from group actions and from vertex partitions.
  1. Quotient graphs from group actions. Harmonic actions and transitive actions are studied separately.
  2. Quotient graphs from paritions. Orbit partitions and equitable partitions are studied.
  3. As a nice application of harmonic morphisms with particularly nice structural properties, we consider multicovers and blowup graphs.
  4. The last section provides explicit formulas for the eigenvalue spectra of harmonic blowups of bipartite graphs, connecting the eigenvalues of the source and target graphs through the blowup parameters. The main result is the Godsil-McKay Theorem.
  5. The chapter ends with exercises and a chapter summary.

Chapter 5 on Graph Morphisms and Graph Products

    This chapter studies graph morphisms associated to tensor products of graphs and lexicographical products of graphs.

    Roughly speaking, a graph product of Gamma1 with Gamma2 is a graph Gamma3 = (V3, E3), where V3 = V1 x V2 is the Cartesian product and there is a rule for the edges E3 based on some conditions on the vertices. The graph products considered in this book are the disjunctive, Cartesian, tensor, lexicographic, and the strong products.

    The most basic questions one wants answered are these:
    is the projection pr1 : Gamma1 x Gamma2 to Gamma1 harmonic, and
    is the projection pr2 : Gamma1 x Gamma2 to Gamma2 harmonic?
    If they do turn out to be harmonic morphisms, we also want to know the vertical and horizontal multiplicities as well. If they do not turn out to be harmonic morphisms, we also want (if possible) to establish conditions on the graphs under which the projections are harmonic.

    However, we want to not only consider products of graphs but also products of morphisms.
    In this case, the most basic question one wants answered is this:
    Given harmonic morphisms phi : Gamma2 to Gamma1 and phi’ : Gamma2′ to Gamma1′, is the
    product phi x phi’ harmonic?

  1. For example, we show that projection morphisms from tensor products are always harmonic with explicit horizontal multiplicity formulas.
  2. Moreover, we prove that the tensor product of harmonic morphisms (without vertical edges) yields a harmonic morphism with horizontal multiplicity matrix given by the Kronecker product of the original multiplicity matrices.
  3. If Gamma x Gamma’ is a lexicographical product then the projection onto the first factor, pr1, is a harmonic morphism. However, the projection onto the second factor is not in general.
  4. We establish a connection between the balanced blowup graph and a lexicographical product. One corollary of this connection is that the blowdown graph agrees with the first projection of the product, so is a harmonic morphism.

Chapter 6 on More Products and Constructions

  1. This chapter studies graph morphisms associated to Cartesian/strong/disjunctive products of graphs as well as joins and NEPS graphs.
  2. For example, we show that projection morphisms from Cartesian products or from strong products are always harmonic with explicit horizontal multiplicity formulas.
  3. Roughly speaking, one of the results states:
    Given two m-quasi-multicovers phi from Gamma2 to Gamma1 and phi’ from Gamma2′ to Gamma1′, the Cartesian product phi x phi’ is also an m-quasi-multicover (hence harmonic).
  4. Another result, roughly speaking, states:
    Given two harmonic morphisms phi from Gamma2 to Gamma1 and phi’ from Gamma2′ to Gamma1′, the
    strong product phi x phi’ is also harmonic.
  5. Can one classify the graphs for which the disjunctive product projections pr1 or pr2 are graph morphisms?
  6. For example, we show that if phi from Gamma2 to Gamma1 and phi’ from Gamma2′ to Gamma1′ are graph morphisms, then the associated product map from Gamma2 x Gamma2′ to Gamma1 x Gamma1′ (where x is the disjunctive product) is, in general, not a graph morphism.
  7. Given two harmonic morphisms phi from Gamma2 to Gamma1 and phi’ from Gamma2′ to Gamma1′, the join morphism phi wedge phi’ is harmonic if and only if a certain technical condition is true.
  8. A theorem due to Urakawa states that projection morphisms from a NEPS graph to one
    of its factors are always harmonic. Moreover, we give explicit horizontal multiplicity formulas.
  9. The chapter ends with exercises and a chapter summary.

Computations are supported throughout using SageMath and Mathematica. The plan is the publish the volume with Birkhauser. We thank the editors there, especially John Benedetto, for their encouragement and guidance.

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.

A simple trace formula for graphs

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Let \Gamma=(V,E) be a simple, connected graph with vertices V={0,1,\dots, n-1} and n\times n adjacency matrix A. We start with the geometric series identity

\frac{1}{I-tA} = \sum_{\ell=0}^\infty t^\ell A^\ell,
where I=I_n is the n\times n identity matrix. Let P denote the orthonormal matrix of normalized eigenvectors, so that

PAP^{-1} = D_\Gamma, D_\Gamma = {diag}(\lambda_1,\dots,\lambda_n),
where diag(…) denotes the diagonal matrix with the given entries on the diagonal. Let the multi-set

Spec(\Gamma)={\lambda_0,\lambda_1\dots,\lambda_{n-1}}
denote the spectrum of \Gamma.

We can conjugate the above equation by P to write

\frac{1}{I-tD_\Gamma}= P\cdot [\sum_{\ell=0}^\infty t^\ell A^\ell]\cdot P^{-1}.
Taking the trace of each side gives

\sum_{j=0}^{n-1} \frac{1}{I-t\lambda_j} = \sum_{\ell=0}^\infty t^\ell tr(A^\ell).
If \Gamma has no eigenvalues equal to 0 (i.e., A is non-singular) then we may also write this as

\sum_{j=0}^{n-1} \frac{\lambda_j^{-1}}{\lambda_j^{-1}-t} = \sum_{\ell=0}^\infty t^\ell tr(A^\ell).

If we multiply both sides of the above equation by a fixed f\in C_c^\infty({\mathbb{R}})
and integrate over t in {\mathbb{R}}, we get,

\sum_{j=0}^{n-1} \lambda_j^{-1}H(f)(\lambda_j^{-1}) = {\frac{1}{\pi}}\sum_{\ell=0}^\infty tr(A^\ell) [M(f)(\ell+1)+(-1)^\ell M(f^*)(\ell+1)],
where H denotes the Hilbert transform

H(f)(z) = \frac{1}{\pi}P.V.\int_{-\infty}^\infty \frac{f(t)}{z-t}\, dt, and M is the Mellin transform

M(f)(z) = \int_{0}^\infty t^{z-1}f(t)\, dt,
and where f^* denotes the negation, f^*(t)=f(-t). Of course, if f is even then M(f)(\ell+1) = M(f^*)(\ell+1), for all \ell.

Note that tr(A^\ell) can be expressed in terms of the number of walks on the graph: If \Gamma is a connected graph and W_\ell=W_\ell(\Gamma) denotes the total number of walks of length \ell on \Gamma then

W_\ell = {\rm tr}(A^\ell)=\sum_{\lambda\in Spec(\Gamma)} \lambda^\ell.

Another mathematician visits the ballpark – WHIP

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This is the second in the series of blog posts inspired by the 2004 Ken Ross book entitled A Mathematician at the Ballpark. The first one is here. In this post, again, we illustrate all these notions using the Baltimore Orioles’ 2022 season.

For an experienced baseball fan, baseball is a game of patterns. We “know” what a well-executed pitch looks like, how a double play is to be executed, how a pop-up is to be fielded, and so on. Because of these expected patterns, we know the plays which should emerge and so the desire to track their occurrences should come as no surprise. It’s been done since professional baseball started in the 1870s.
This week we discuss a pitching statistic you see on televised games, WHIP. WHIP is short for “walks plus hits allowed per innings pitched”.

Earned run average

We start off with the most basic pitching statistic, the Earned Run Average or ERA. This is the number of earned runs per 9 innings pitched:

ERA = 9·ER/IP,

where 

  • IP, the number of Innings Pitched,
  • ER, the number of Earned Runs allowed by the pitcher. That is, it counts the number of runs enabled by the offensive team’s production in the face of competent play from the defensive team.

It is possible to have ERA = ∞, since innings are measured by the number of outs achieved (so if the pitcher doesn’t get any batters out, his IP=0). The lower the ERA the better the pitcher. In the 2022 season, right-handed Félix Bautista, who entered late innings as either a closer or a reliever, had an ERA of 2.19. Left-handed closer Cionel Pérez had an ERA of 1.40.

WHIP

We define walks plus hits allowed per innings pitched by:

WHIP = (BB+H)/IP,

where (as in the previous post) BB is the number of walks and H stands for the number of Hits allowed by the pitcher (so, for example, reaching base due to a fielding error doesn’t count). WHIP reflects a pitcher’s propensity for allowing batters to reach base, therefore a lower WHIP indicates better performance.

When we plot the ERA vs the WHIP for the top 20 Orioles pitchers in 2022, we get 

Again, the line shown is the line that best fits the data. As the line of best fit doesn’t fit the date too well, this tells us that these two statistical measurements aren’t too well-correlated. In other words, low ERA indicates a good pitcher and low WHIP indicates a good pitcher, but the values for “average” pitchers seem less related to each other.

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. 

Harmonic quotients of regular graphs – examples

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Caroline Melles and I have written a preprint that collects numerous examples of harmonic quotient morphisms \phi : \Gamma_2 \to \Gamma_1, where \Gamma_1=\Gamma_2/G is a quotient graph obtained from some subgroup G \subset Aut(\Gamma_2). The examples are for graphs having a small number of vertices (no more than 12). For the most part, we also focused on regular graphs with small degree (no more than 5). They were all computed using SageMath and a module of special purpose Python functions I’ve written (available on request). I’ve not counted, but the number of examples is relatively large, maybe over one hundred.

I’ll post it to the math arxiv at some point but if you are interested now, here’s a copy: click here for pdf.