Robert M. Freund


Robert M. Freund

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Robert Freund is the Theresa Seley Professor in Management Science and a Professor of Operations Research at the MIT Sloan School of Management.  

His main research interests are in convex optimization, computational complexity and related computational science, convex geometry, large-scale nonlinear optimization, and related mathematical systems.  His more recent work is in first-order methods and their connections to statistical and machine learning. He has served as coeditor of the journal Mathematical Programming and associate editor of several optimization and operations research journals. He is the former CoDirector of MIT Operations Research Center, the MIT Program in Computation for Design and Optimization, and the former Chair of the INFORMS Optimization Section. He also served a term as Deputy Dean of the Sloan School at MIT (2008-11).

Freund received the Longuet-Higgins Prize in computer vision (2007) as well as numerous teaching and education awards at MIT in conjunction with the course and textbook (coauthored with Dimitris Bertsimas) Data, Models, and Decisions:  the Fundamentals of Management Science.

Freund holds a BA in mathematics from Princeton University and an MS and a PhD in operations research from Stanford University. 


Freund wins MIT’s 2020 Seegal prize

Freund elected as an INFORMS Fellow


"An 'Oblivious' Ellipsoid Algorithm for Solving a System of (In)Feasible Linear Inequalities."

Lamperski, Jourdain, Robert M. Freund, and Michael J. Todd (submitted), MIT Sloan Working Paper 5892-19. Cambridge, MA: MIT Sloan School of Management, October 2019.

"Accelerated Residual Methods for the Iterative Solution of Systems of Equations."

Nguyen, Ngoc Cuong, Pablo Fernandez, Robert M. Freund, and Jaime Peraire. SIAM Journal of Scientific Computing Vol. 40, No. 5 (2018): A3157-79.

"New Computational Guarantees for Solving Convex Optimization Problems with First Order Methods, via a Function Growth Condition Measure."

Freund, Robert M., and Haihao Lu. Mathematical Programming Vol. 170, No. 2 (2018): 445-477.

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