<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Worst-Case on Aayush Bajaj's Augmenting Infrastructure</title><link>https://abaj.ai/tags/worst-case/</link><description>Recent content in Worst-Case on Aayush Bajaj's Augmenting Infrastructure</description><generator>Hugo</generator><language>en</language><copyright>© 2026 Aayush Bajaj</copyright><lastBuildDate>Fri, 10 Jul 2026 08:15:43 +1000</lastBuildDate><atom:link href="https://abaj.ai/tags/worst-case/index.xml" rel="self" type="application/rss+xml"/><item><title>Robust</title><link>https://abaj.ai/wiki/ccs/programming/paradigms/robust/</link><pubDate>Fri, 10 Jul 2026 07:43:56 +1000</pubDate><guid>https://abaj.ai/wiki/ccs/programming/paradigms/robust/</guid><description>&lt;p>every &lt;a
 href="https://abaj.ai/wiki/ccs/programming/paradigms/linear/"
 
 
>linear program&lt;/a> you have ever written down was a lie: the coefficients came from measurements, forecasts and vendor spreadsheets, and the optimal vertex — sitting, by design, on the boundary of the feasible region — shatters the moment any of them wobbles.&lt;span class="margin-note" data-note="an optimal basic solution binds n constraints with zero slack; it is maximally exposed to data error by construction">
 &lt;span class="margin-note-indicator">𐃏&lt;/span>
&lt;/span>

robust optimisation is the pessimist&amp;rsquo;s response: declare a set \(\mathcal{U}\) of realisations you refuse to be hurt by, and demand feasibility for &lt;em>every&lt;/em> member of it. no distributions, no expectations, no scenarios — just a set and a worst case. the surprise, and the reason the field exists, is that this worst case can usually be folded back into a deterministic problem of the same (or nearly the same) complexity class.&lt;sup id="fnref:1">&lt;a href="#fn:1" class="footnote-ref" role="doc-noteref">1&lt;/a>&lt;/sup>&lt;/p></description></item></channel></rss>