Applied Algebra Seminar Spring 2021

When: 1:30pm, Thursdays

Where: Virtual

List: mathaas@lists.wisc.edu, to join email join-mathaas@lists.wisc.edu

Contact: Shamgar Gurevich, Jose Israel Rodriguez, Julia Lindberg (Lead)

Remark: This seminar is held on the fourth Thursday of the month

Spring 2021 Schedule

date	speaker	title	host(s)
February 25	TBD
March 26	James Saunderson (Monash University)	TBD	Virtual
April 22	TBD

Spring 2020 Schedule

date	speaker	title	host(s)
February 20	Carla Michini (UW Madison)	Short simplex paths in lattice polytopes	Local
March 5	Alisha Zachariah (UW Madison)	Efficient Estimation of a Sparse Delay-Doopler Channel	Local
March 19	Spring Break
March 26	(Seminar on Hiatus because of Covid-19)

Abstracts

Carla Michini

Short simplex paths in lattice polytopes

We consider the problem of optimizing a linear function over a lattice polytope P contained in [0,k]^n and defined via m linear inequalities. We design a simplex algorithm that, given an initial vertex, reaches an optimal vertex by tracing a path along the edges of P of length at most O(n^6 k log k). The length of this path is independent on m and is the best possible up to a polynomial function, since it is only polynomially far from the worst case diameter. The number of arithmetic operations needed to compute the next vertex in the path is polynomial in n, m and log k. If k is polynomially bounded by n and m, the algorithm runs in strongly polynomial time. This is a joint work with Alberto Del Pia.

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Alisha Zachariah

Efficiently Estimating a Sparse Delay-Doppler Channel

Multiple wireless sensing tasks, e.g., radar detection for driver safety, involve estimating the ”channel” or relationship between signal transmitted and received. In this talk, I will focus on a certain type of channel known as the delay-doppler channel. This channel model starts to be applicable in high frequency carrier settings, which are increasingly common with recent developments in mmWave technology. Moreover, in this setting, both the channel model and existing technologies are amenable to working with signals of large bandwidth, and using such signals is a standard approach to achieving high resolution channel estimation. However, when high resolution is desirable, this approach creates a tension with the desire for efficiency because, in particular, it immediately implies that the signals in play live in a space of very high dimension N (e.g., ~10^6 in some applications), as per the Shannon-Nyquist sampling theorem.

To address this, I will propose a randomized algorithm for channel estimation in the k-sparse setting (e.g., k objects in radar detection), with sampling and space complexity both on the order of k(log N)^2, and arithmetic complexity on the order of k(log N)^3+k^2, for N sufficiently large.

While this algorithm seems to be extremely efficient -- to the best of our knowledge, the first of this nature in terms of complexity -- it is just a simple combination of three ingredients, two of which are well-known and widely used, namely digital chirp signals and discrete Gaussian filter functions, and the third being recent developments in Sparse Fast Fourier Transform algorithms.

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Other events to note

date	event/title	speaker
February 7, 2020	Talk: Inverse Problems, Imaging and Tensor Decomposition	Joe Kileel (Princeton)
February 10. 2020	Talk: Matroids, log-concavity, and expanders	Cynthia Vinzant (NCSU)
April 17, 2020	/// Applied Algebra Days 4 - Tensors	Several talks on tensors (Postponed)

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