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X-WR-CALNAME;VALUE=TEXT:Seminar: Frederick Manners (University of California, San Diego): "Inverse theorems and approximate structure"
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SUMMARY:Seminar: Frederick Manners (University of California, San Diego): "Inverse theorems and approximate structure"
DESCRIPTION:<p>	<drupal-media data-entity-type="media" data-entity-uuid="7d2848c6-f2e9-4b58-97b7-85e7276c3969" alt="Frederick Manners" data-view-mode="hwp_small"></drupal-media><br>Zoom link: <a href="https://harvard.zoom.us/j/779283357?pwd=MitXVm1pYUlJVzZqT3lwV2pCT1ZUQT09">https://harvard.zoom.us/j/779283357?pwd=MitXVm1pYUlJVzZqT3lwV2pCT1ZUQT09</a><br>Speaker: Frederick Manners (University of California, San Diego)<br>Title: <strong>Inverse theorems and approximate structure</strong><br>Abstract: We call a function f linear if f(x+y) = f(x) + f(y) holds for all x,y.  It is natural to call f "99% linear" if instead this identity holds for most pairs (x,y); say, 99% of pairs. Similarly, we could say f is "1% linear" if this identity holds 1% of the time.  A natural question is then: what can we say about the structure of "99% linear" or "1% linear" functions?  Are they always just perturbations of true 100% linear functions, or are there other examples?<br> <br>Given almost any algebraic definition, you can similarly ask about its approximate variants, and if you can prove a strong positive statement, it tends to have applications.  In particular, I will discuss how 1% linear functions relate to the Polynomial Freiman-Ruzsa conjecture, and how 1% polynomial functions relate to the Inverse Theorem for the Gowers norms.</p><p>	 </p><p>	 </p>
LOCATION:Jefferson 256 and Zoom
STATUS:CONFIRMED
DTSTART:20240130T213000Z
DTEND:20240130T223000Z
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