The Macroeconomic Impact of Climate Change: Global vs. Local Temperature | Diego Kanzig (Northwestern)

Ten minutes for your mind@2:50 every day at 2:50 pm in multiple time zones:Europa@2:50, EET, Athens, Helsinki (UTC+2) (7:50 am EST) https://us02web.zoom.us/j/88298032734Atlantica@2:50, EST, New York, Toronto (UTC-4) https://us02web.zoom.us/j/85349851047Pacifica@2:50, PST, Los Angeles, Vancouver (UTC=7) (5:50 pm EST) https://us02web.zoom.us/j/85743543699Almost everything works better again if you unplug it for a bit, including your mind. Stop by and unplug. Get the benefits of mindfulness without the fuss.@2:50 meets at the same time every single day for ten minutes of quiet together.No pre-requisite, no registration needed.Visit the website to view all @2:50 time zones each day.at250.org or at250.mit.edu

Ryota Iijima Princeton University (joint with Mira Frick and Daisuke Oyama)

Alexandre Belloni (Duke University)

A series of talks on OR-related topics. For more information see: https://orc.mit.edu/seminars-events/

Speaker: Mike O'Neil (Courant Institute)Title: Fast Direct Solvers: Foundations and ChallengesAbstract: Fast Direct Solvers (FDS) address the problem of solving a system of linear equations 𝐴𝑥 = 𝑏 arising from the discretization of either an elliptic PDE or of an associated integral equation. The matrix 𝐴 will be sparse when the PDE is discretized directly, and dense when an integral equation formulation is used. For decades, industry practice for large scale problems has been to use iterative solvers such as multigrid, GMRES, or conjugate gradients. In con- trast, a direct solver builds an approximation to the inverse of 𝐴 or an easily invertible factorization of 𝐴 (e.g. LU or Cholesky). A major development in numerical analysis in the last couple of decades has been the emergence of algorithms for constructing such factorizations or performing such inver- sions in linear or close to linear time. Such methods must necessarily exploit that the inverse of 𝐴 is “data-sparse,” e.g. that it can be tessellated into blocks that have low numerical rank. This talk will cover the development of FDS’s for both sparse and dense matrices, recent developments in the field, as well as future challenges and opportunities.