Date/Time : 31 Jan 2024 15:00
Venue : S17 #04-05 (SR2)
Speaker : Matteo Mucciconi, University of Warwick

The deformed polynuclear growth is a growth process that generalizes the polynuclear growth studied in the context of KPZ universality class. In this talk, I will discuss the mathematical derivation of large time large deviations for the height function. Rare events, as functions of the time t, display distinct decay rates based on whether the height function grows significantly larger (upper tail) or smaller (lower tail) than the expected value. Upper tails exhibit an exponential decay with rate function which we determine explicitly. Conversely, the lower tails experience a more rapid decay and the rate function is given in terms of a variational problem.

Our analysis relies on two inputs. The first is a connection between the height function h and an important measure on the set of integer partitions, the Poissonized Plancherel measure, which stems from nontrivial applications of the celebrated Robinson-Schensted-Knuth correspondence. The second ingredient is the derivation of a priori convexity bounds for the rate function, which combines combinatorial and coupling arguments.

This is a joint work with S.Das (Chicago) and Y.Liao (Wisconsin-Madison).

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Date/Time : 29 Jan 2024 10:00
Venue : S17 #04-06 (SR1)
Speaker : Jimmy He, Massachusetts Institute of Technology

Random growth models in 1+1 dimension capture the behavior of interfaces evolving in the presence of noise. These models are expected to exhibit universal behavior, but we are still far from proving such results even in relatively simple models. A key development which has led to recent progress is the discovery of exact formulas for certain models with rich algebraic structure, leading to asymptotic results. I will survey some of these results, with a focus on models where a single boundary wall is present, as well as applications to rates of convergence for a Markov chain.

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Date/Time : 22 Jan 2024 15:00
Venue : S17 #04-06 (SR1)
Speaker : Yang Lei, Institute for Advanced Study

I will talk about recent progress in the study of quantitative equidistribution of unipotent orbits in homogeneous spaces, namely, effective versions of Ratner’s equidistribution theorem. I will sketch the main idea of my proof for unipotent orbits in SL(3,R)/SL(3,Z). The proof combines new ideas from harmonic analysis and incidence geometry. In particular, the quantitative behavior of unipotent orbits is closely related to a Kakeya model.

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Date/Time : 15 Jan 2024 15:00
Venue : S17 #04-06 (SR1)
Speaker : Liu Jialin, Alibaba Group (U.S.) Inc, Damo Academy

AI and data science have demonstrated remarkable promise in enhancing optimization algorithms. Utilizing AI/ML techniques, we aim to complement and potentially enhance traditional optimization methods, offering improvements in aspects like computational speed and solution quality. Despite these advancements, a deep, systematic understanding of these methods remains underdeveloped. In this talk, the speaker will demonstrate through concrete examples how mathematical tools, particularly optimization theory, can be used to unravel the mysteries of these ‘black boxes’. The speaker will also discuss the development of interpretable and reliable AI models for optimization, grounded in these mathematical principles.

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Date/Time : 11 Jan 2024 10:00
Venue : via Zoom
Speaker : Shaoning Han, University of Southern California

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https://nus-sg.zoom.us/j/83242625129?pwd=ZmRzME93QndWbTNZQUdPUVdIS080Zz09

Meeting ID: 832 4262 5129
Passcode: 157423

Many regression problems arising in modern data science involve combinatorial decisions. For instance, sparsity is often introduced into statistical models to enhance interpretability and generalization. Outlier detection before training can contribute to model robustness. When cast as optimization problems, they are typically NP-hard, posing significant computational hurdles. To ease the computational burden, practitioners often either resort to much easier convex surrogates, albeit at the cost of sacrificing model fidelity, or rely on heuristics that lack theoretical guarantees unless stringent (sub)optimality conditions are met. This presentation provides insights into understanding and solving combinatorial regression problems through the lens of mixed-integer nonlinear programming. The first part focuses on developing convexification techniques that are applicable to general objectives with low-rank decompositions. In the second part, we explore solution methods tailored for a specific class of regression problems defined over graphs.

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Date/Time : 19 Dec 2023 15:00
Venue : S17 #04-06 (SR1)
Speaker : Zhuan Koh, The national research institute for mathematics and computer science (Netherlands)

Linear programs (LPs) can be solved in polynomial time using the ellipsoid algorithm or interior point methods (IPMs). However, it is a major open question whether LPs can be solved in strongly polynomial time. Given an LP with m inequalities and n variables, a strongly polynomial algorithm must compute an optimal solution using poly(m,n) elementary arithmetic operations (+,-,*,/,<?). In particular, the number of such operations cannot depend on the size of coefficients describing the LP. This question was first asked by Megiddo in 1983, and listed as one of Smale’s problems for mathematics in the 21st century. In this talk, I will cover recent progress on this open problem. By combining circuits from combinatorial optimization and IPMs from continuous optimization, we provide an affirmative answer to this question when every inequality has at most two variables.

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Date/Time : 14 Dec 2023 16:20
Venue : S17 #04-06 (SR1)
Speaker : Xiao Manyu, Northwestern Polytechnical University

The common issue of surrogate models is to make good use of sampling data. In theory, the higher the fidelity of sampling data obtained, the more accurate the approximation model built. However, in practical engineering problems, high-fidelity data may be less available, and such data may also be computationally expensive. On
the contrary, we often obtain low-fidelity data under certain simplifications. Although low-fidelity data is less accurate, such data still contains much information about the real system. So, combining both high- and low-fidelity data in the construction of a surrogate model may lead to better representation of the physical phenomena.

In this work, the original principle of Multi-Fidelity Proper Orthogonal Decomposition will consist in projecting the high-dimensional system of equations into a low-dimensional space, with reduced basis vectors. These vectors are updated with incremental “on-the-fly” merges between costly high-fidelity simulation snapshots and more rapid lower-fidelity vectors. Successful achievements of this work will lead to potential benefits in the fields of large-scale mechanical engineering.

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Date/Time : 14 Dec 2023 15:00
Venue : S17 #04-06 (SR1)
Speaker : Wang Ligong, Northwestern Polytechnical University

Let \mu(G) denote the spectral radius of a graph G. We partly confirm a conjecture due to Nikiforov, which is a spectral radius analogue of the well-known Erdos-Sos Conjecture that any tree of order t is contained in a graph of average degree greater than t − 2. Let S_{n,k} be the graph obtained by joining every vertex of a complete graph on k vertices to every vertex of an independent set of order n−k, and let S_{n,k}^+ be the graph obtained from S_{n,k} by adding a single edge joining two vertices of the independent set of S_{n,k}. In 2010, Nikiforov conjectured that for a given integer k, every graph G of sufficiently large order n with \mu(G) \geq \mu(S_{n,k}^+) contains all trees of order 2k + 3, unless G = S_{n,k}^+. We confirm this conjecture for trees with diameter at most four, with one exception. In fact, we prove the following stronger result for k \geq 8. If a graph G with sufficiently large order n satisfies \mu(G) ≥ \mu(S_{n,k}) and G \neq S_{n,k}, then G contains all trees of order 2k + 3 with diameter at most four, except for the tree obtained from a star on k + 2 vertices by subdividing each of its k + 1 edges once. We also introduce some new results about the above conjecture. This is a joint work with Xiangxiang Liu and Hajo Broersma.

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Date/Time : 14 Dec 2023 15:40
Venue : S17 #04-06 (SR1)
Speaker : Bai Xueli, Northwestern Polytechnical University

As a first attempt to understand the effects of temporal periodicity on dynamics of general periodic parabolic reaction-diffusion systems, we analyze the asymptotic behaviour of the principal eigenvalue for linear cooperative/competition periodic-parabolic systems with small/large diffusion rates and then discuss local and global stability of related periodic-parabolic Lotka Volterra competition diffusion system. This is a joint work with Xiaoqing He and Wei-Ming Ni.

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