**Jason Freitas and Joshua Huang**

**Mentor: ****Oleksii Mostovyi**

**Jason Freitas and Joshua Huang**

**Mentor: ****Oleksii Mostovyi**

REU participants:

Bobita Atkins, Massachusetts College of Liberal Arts

Ashka Dalal, Rose-Hulman Institute of Technology

Natalie Dinin, California State University, Chico

Jonathan Kerby-White, Indiana University Bloomington

Tess McGuinness, University of Connecticut

Tonya Patricks, University of Central Florida

Genevieve Romanelli, Tufts University

Yiheng Su, Colby College

Mentors: Bernard Akwei, Rachel Bailey, Luke Rogers, Alexander Teplyaev

**Convergence, optimization**** and stabilization**** of singular eigenmaps**

B.Akwei, B.Atkins, R.Bailey, A.Dalal, N.Dinin, J.Kerby-White, T.McGuinness, T.Patricks, L.Rogers, G.Romanelli, Y.Su, A.Teplyaev

Eigenmaps are important in analysis, geometry and machine learning, especially in nonlinear dimension reduction.

Versions of the Laplacian eigenmaps of Belkin and Niyogi are a widely used nonlinear dimension reduction technique in data analysis. Data points in a high dimensional space \(\mathbb{R}^N\) are treated as vertices of a graph, for example by taking edges between points separated by distance at most a threshold \(\epsilon\) or by joining each vertex to its \(k\) nearest neighbors. A small number \(D\) of eigenfunctions of the graph Laplacian are then taken as coordinates for the data, defining an eigenmap to \(\mathbb{R}^D\). This method was motivated by an intuitive argument suggesting that if the original data consisted of \(n\) sufficiently well-distributed points on a nice manifold \(M\) then the eigenmap would preserve geometric features of \(M\).

Several authors have developed rigorous results on the geometric properties of eigenmaps, using a number of different assumptions on the manner in which the points are distributed, as well as hypotheses involving, for example, the smoothness of the manifold and bounds on its curvature. Typically, they use the idea that under smoothness and curvature assumptions one can approximate the Laplace-Beltrami operator of \(M\) by an operator giving the difference of the function value and its average over balls of a sufficiently small size \(\epsilon\), and that this difference operator can be approximated by graph Laplacian operators provided that the \(n\) points are sufficiently well distributed.

In the present work we consider several model situations where eigen-coordinates can be computed analytically as well as numerically, including the intervals with uniform and weighted measures, square, torus, sphere, and the Sierpinski gasket. On these examples we investigate the connections between eigenmaps and orthogonal polynomials, how to determine the optimal value of \(\epsilon\) for a given \(n\) and prescribed point distribution, and the dependence and stability of the method when the choice of Laplacian is varied. These examples are intended to serve as model cases for later research on the corresponding problems for eigenmaps on weighted Riemannian manifolds, possibly with boundary, and on some metric measure spaces, including fractals.

Approximation of the eigenmaps of a Laplace operator depends crucially on the scaling parameter \(\epsilon\). If \(\epsilon\) is too small or too large, then the approximation is inaccurate or completely breaks down. However, an analytic expression for the optimal \(\epsilon\) is out of reach. In our work, we use some explicitly solvable models and Monte Carlo simulations to find the approximately optimal value of \(\epsilon\) that gives, on average, the most accurate approximation of the eigenmaps.

Our study is primarily inspired by the work of Belkin and Niyogi “*Towards a theoretical foundation for Laplacian-based manifold methods*.”

Talk: Laplacian Eigenmaps and Chebyshev Polynomials

Talk: A Numerical Investigation of Laplacian Eigenmaps

Talk: Analysis of Averaging Operators

Intro Text: Graph Laplacains, eigen-coordinates, Chebyshev polynomials, and Robin problems

Intro Text: A Numerical Investigation of Laplacian Eigenmaps

Intro Text: Comparing Laplacian with the Averaging Operator

Poster: Laplacian Eigenmaps and Orthogonal Polynomials

Results are presented at the 2023 Young Mathematicians Conference (YMC) at the Ohio State University, a premier annual conference for undergraduate research in mathematics, and at the 2024 Joint Mathematics Meetings (JMM) in San Francisco, the largest mathematics gathering in the world.

**Group Members:** Tyler Campos, Andrew Gannon, Benjamin Hanzsek-Brill, Connor Marrs, Alexander Neuschotz, Trent Rabe and Ethan Winters.

**Mentors:** Rachel Bailey, Fabrice Baudoin, Masha Gordina

**Overview:** We study and simulate on computers the fractional Gaussian fields and their discretizations on surfaces like the two-dimensional sphere or two-dimensional torus. The study of the maxima of those processes will be done and conjectures formulated concerning limit laws. Particular attention will be paid to log-correlated fields (the so-called Gaussian free field).

“Paths of the Fractional Gaussian Field on S1 and the Torus” by Andrew Gannon (University of Connecticut)

**Fair Pricing and Hedging Under Small Perturbations of the Numéraire on a Finite Probability Space**

Involve (2022), Vol. 15(4), pp. 649-668. [published version] [arXiv]

accepted in the Missouri Journal of Mathematical Sciences (2023)

**Séminaire Lotharingien de Combinatoire (2021)**

Sém. Lothar. Combin.85B(2021), Art. 14, 12 pp.

**Proceedings of the 33rd Conference on Formal Power****Series and Algebraic Combinatorics ****Ben Drucker, Eli Garcia, Emily Gunawan, and Rose Silver**

A box-ball system is a collection of discrete time states representing a permutation,

on which there is an action called a BBS move. After a finite number of BBS moves

the system decomposes into a collection of soliton states; these are weakly

increasing and invariant under BBS moves. The students proved that when this

collection of soliton states is a Young tableau or coincides with a partition of a type

described by Robinson-Schensted (RS), then it is an RS insertion tableau. They also

studied the number of steps required to reach this state.

Sarah Boese, Tracy Cui, Sam Johnston

Gianmarco Molino, Olekisii Mostovyi

In practice, financial models are not exact — as in any field, modeling based on real data introduces some degree of error. However, we must consider the effect error has on the calculations and assumptions we make on the model. In complete markets, optimal hedging strategies can be found for derivative securities; for example, the recursive hedging formula introduced in Steven Shreve’s “Stochastic Calculus for Finance I” gives an exact expression in the binomial asset model, and as a result the unique arbitrage-free price can be computed at any time for any derivative security.

In incomplete markets this cannot be accomplished; one possibility for computing optimal hedging strategies is the method of sequential regression. We considered this in discrete-time; in the (complete) binomial model we showed that the strategy of sequential regression introduced by Follmer and Schweizer is equivalent to Shreve’s recursive hedging formula, and in the (incomplete) trinomial model we both explicitly computed the optimal hedging strategy predicted by the Follmer-Schweizer decomposition and we showed that the strategy is stable under small perturbations.

Cory McCartan, Laura LeGare, Caitlin Davis.

Geodesics (shortest paths) on manifolds such as planes and spheres are well understood. Geodesics on fractal sets such as the Sierpinski Triangle are much more complicated. We begin by constructing algorithms for building shortest paths and provide explicit formulas for computing their lengths. We then turn to the question of interpolation along geodesics—given two subsets of the Sierpinski Triangle, we “slide” points in one set along geodesics to the other set. We construct a measure along the interpolated sets which formalizes a notion of the interpolation of a distribution of mass, and we prove interesting self-similarity relations about this measure.

Two of our REU (2017 Stochastics) participants, Raji Majumdar and Anthony Sisti, will be presenting posters Applications of Multiplicative LLN and CLT for Random Matrices and Black Scholes using the Central Limit Theorem on Friday, January 12 at the MAA Student Poster Session, and both of them will be giving talks on Saturday, January 13 at the AMS Contributed Paper Session on Research in Applied Mathematics by Undergraduate and Post-Baccalaureate Students.

Their travel to the 2018 JMM has been made possible with the support of the MAA and UConn’s OUR travel grants.