Difference between revisions of "Applied Algebra Seminar Spring 2020"
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|[https://www.math.wisc.edu/wiki/index.php/Applied_Algebra_Days_Tensors /// ]
Revision as of 14:28, 25 February 2020
When: 11:00am, Thursdays
Where: 901 Van Vleck Hall
List: firstname.lastname@example.org, to join email email@example.com
Contact: Shamgar Gurevich, Jose Israel Rodriguez
Remark: We usually have a gap of around 2-3 weeks between seminars.
Spring 2020 Schedule
|February 20||Carla Michini (UW Madison)||Short simplex paths in lattice polytopes||Local|
|March 12||Alisha Zachariah (UW Madison)||Efficient Estimation of a Sparse Delay-Doopler Channel||Local|
|March 19||Spring Break|
|March 26||(Organizer traveling)|
|April 16||JM Landsberg (Texas A&M)||TBD||Jose|
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.
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.
Other events to note
|February 7||Talk: Inverse Problems, Imaging and Tensor Decomposition||Joe Kileel (Princeton)|
|February 10||Talk: Matroids, log-concavity, and expanders||Cynthia Vinzant (NCSU)|
|April 17||/// Tensor Day||Several talks on tensors|