Newsroom
Combinatorial Optimization
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FDS Colloquium: Lorenzo Orecchia (Chicago), “Variational Characterizations of First-Order Algorithms via Self-Duality”
Talk summary: First-order methods for convex optimization play an important role in the efficient deployment of machine learning algorithms. While a large number of […]
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FDS Colloquium: Pravesh Kothari (Princeton), “The surprising reach of spectral algorithms for smoothed k-SAT”
Abstract: Semirandom input models are hybrids of the classical worst-case and average-case models in algorithm design. They were introduced in the 1990s to inspire […]
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FDS Colloquium: Brice Huang (MIT), “Algorithmic thresholds in random optimization problems”
Abstract: Optimizing high-dimensional functions generated from random data is a central problem in modern statistics and machine learning. As these objectives are highly non-convex, […]
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FDS Colloquium: Bento Natura (Columbia), “Faster Exact Linear Programming”
Optional Zoom link: https://yale.zoom.us/j/99342713421 Abstract: We present a novel algorithm to solve various subclasses of linear programs, with a particular focus on strongly polynomial […]