%0 Journal Article %1 tropp2015introduction %A Tropp, Joel A. %D 2015 %K book foundations mathematics probability stats theory %T An Introduction to Matrix Concentration Inequalities %U http://arxiv.org/abs/1501.01571 %X In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (formerly) challenging problems with a page or two of arithmetic. The aim of this monograph is to describe the most successful methods from this area along with some interesting examples that these techniques can illuminate.

@article{tropp2015introduction, abstract = {In recent years, random matrices have come to play a major role in computational mathematics, but most of the classical areas of random matrix theory remain the province of experts. Over the last decade, with the advent of matrix concentration inequalities, research has advanced to the point where we can conquer many (formerly) challenging problems with a page or two of arithmetic. The aim of this monograph is to describe the most successful methods from this area along with some interesting examples that these techniques can illuminate.}, added-at = {2019-06-25T15:09:25.000+0200}, author = {Tropp, Joel A.}, biburl = {https://www.bibsonomy.org/bibtex/22504ce91b94b04622e2f33ca3242ae70/kirk86}, description = {[1501.01571] An Introduction to Matrix Concentration Inequalities}, interhash = {2f18b953eabc9986770ba2974d518039}, intrahash = {2504ce91b94b04622e2f33ca3242ae70}, keywords = {book foundations mathematics probability stats theory}, note = {cite arxiv:1501.01571Comment: 163 pages. To appear in Foundations and Trends in Machine Learning}, timestamp = {2019-06-25T15:09:25.000+0200}, title = {An Introduction to Matrix Concentration Inequalities}, url = {http://arxiv.org/abs/1501.01571}, year = 2015 }