Practice-friendly theory and algorithms for linear regression problems

Abstract in Lay TermsTitle in lay terms: Advanced Methods for Solving Linear RegressionNSF Proposal Number: 1814041BSF Proposal Number: 2017698PrincipalInvestigators: HaimAvron(TelAvivUniversity), PetrosDrineas(Pur-due University), Ioannis Koutis (New Jersey Institute of Technology)One of the most fundamental computational method employed by scientists is the so-lution of linear regression. The problem is broadly applicable in science and engineering.

It is also of great interest for a computer science perspective, as it will naturally lead toadvances in designing new methods for many fundamental problems. This proposal seeksto design new, provably fast and accurate algorithms for solving linear regression problemsusing randomization, an exciting techniques that has proven itself very useful in the pastfew years in the context of solving linear regression problems.