Michael P. Friedlander

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Ewout van den BergIBM T.J. Watson Research CenterVerified email at us.ibm.com
Mark SchmidtProfessor of Computer Science, University of British ColumbiaVerified email at cs.ubc.ca
Zhenan FanStaff Researcher at Huawei Technologies CanadaVerified email at cs.ubc.ca
Aleksandr AravkinUniversity of WashingtonVerified email at uw.edu
Felix J. HerrmannProfessor Schools of Earth and Atmospheric Sciences, Computational Science and EngineeringVerified email at gatech.edu
Ozgur YilmazProfessor, Mathematics, University of British ColumbiaVerified email at math.ubc.ca
Fang HuangUniversity of British ColumbiaVerified email at cs.ubc.ca
Michael SaundersProfessor of Operations Research, Stanford UniversityVerified email at stanford.edu
Gabriel GohOpenAIVerified email at openai.com
Kevin MurphyResearch Scientist, GoogleVerified email at google.com
Rayan SaabProfessor, Mathematics & Data Science, UC San DiegoVerified email at ucsd.edu
James V BurkeUniversity of WashingtonVerified email at uw.edu
Gilles Hennenfent, Ph.D.Earth Scientist - ChevronVerified email at chevron.com
Ives MacedoAdaVerified email at ada.support
Dominique OrbanProfessor of Computational Mathematics, GERAD and Ecole Polytechnique de MontrealVerified email at gerad.ca
Ting Kei Pong (龐鼎基)Professor, the Hong Kong Polytechnic UniversityVerified email at polyu.edu.hk
Yifan SunAssistant Prof at Stonybrook CS DeptVerified email at stonybrook.edu
Zirui ZhouHuawei Technologies CanadaVerified email at huawei.com
Jian PeiArthur S. Pearse Distinguished Professor, Duke UniversityVerified email at cs.sfu.ca
Yong ZhangDistinguished Researcher at Huawei Technologies, CanadaVerified email at huawei.com

Michael P. Friedlander

Professor of Computer Science and Mathematics, University of British Columbia

Verified email at friedlander.io - Homepage

optimization convex optimization mathematical programming computational mathematics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Probing the Pareto frontier for basis pursuit solutions E van den Berg, MP Friedlander SIAM Journal on Scientific Computing 31 (2), 890-912, 2008	2421	2008
SPGL1: A solver for large-scale sparse reconstruction E van den Berg, MP Friedlander https://friedlander.io/spgl1/, 2007	660*	2007
Hybrid deterministic-stochastic methods for data fitting MP Friedlander, M Schmidt SIAM Journal on Scientific Computing 34 (3), A1380-A1405, 2012	486	2012
Optimizing costly functions with simple constraints: A limited-memory projected quasi-Newton algorithm M Schmidt, E van den Berg, MP Friedlander, K Murphy Artificial Intelligence and Statistics, 456-463, 2009	341	2009
Sparse optimization with least-squares constraints E van den Berg, MP Friedlander SIAM Journal on Optimization 21 (4), 1201-1229, 2011	302	2011
Coordinate descent converges faster with the gauss-southwell rule than random selection J Nutini, M Schmidt, I Laradji, M Friedlander, H Koepke International Conference on Machine Learning, 1632-1641, 2015	281	2015
Recovering compressively sampled signals using partial support information M Friedlander, H Mansour, R Saab, O Yilmaz Information Theory, IEEE Transactions on 58 (2), 1122-1134, 2010	270	2010
Satisfying real-world goals with dataset constraints G Goh, A Cotter, M Gupta, MP Friedlander Advances in neural information processing systems 29, 2016	264	2016
Theoretical and empirical results for recovery from multiple measurements E van den Berg, MP Friedlander IEEE Transactions on Information Theory 56 (5), 2516-2527, 2010	156	2010
Exact regularization of convex programs MP Friedlander, P Tseng SIAM Journal on Optimization 18 (4), 1326-1350, 2007	152	2007
Fighting the curse of dimensionality: compressive sensing in exploration seismology F Herrmann, MP Friedlander, O Yılmaz IEEE Signal Processing Magazine 29 (3), 88-100, 2011	149	2011
A two-sided relaxation scheme for mathematical programs with equilibrium constraints V DeMiguel, MP Friedlander, FJ Nogales, S Scholtes SIAM Journal on Optimization 16 (2), 587-609, 2005	136*	2005
Joint-sparse recovery from multiple measurements E van den Berg, MP Friedlander arXiv preprint arXiv:0904.2051, 2009	119	2009
A primal–dual regularized interior-point method for convex quadratic programs MP Friedlander, D Orban Mathematical Programming Computation 4, 71-107, 2012	109	2012
Group sparsity via linear-time projection E van den Berg, M Schmidt, MP Friedlander, K Murphy Dept. Comput. Sci., Univ. British Columbia, Vancouver, BC, Canada, 2008	108	2008
Computing non-negative tensor factorizations MP Friedlander, K Hatz Optimisation Methods and Software 23 (4), 631-647, 2008	106	2008
Robust inversion, dimensionality reduction, and randomized sampling A Aravkin, MP Friedlander, FJ Herrmann, T Van Leeuwen Mathematical Programming 134, 101-125, 2012	98	2012
Sparco: A testing framework for sparse reconstruction E van den Berg, MP Friedlander, G Hennenfent, F Herrmann, R Saab, ... Dept. Comput. Sci., Univ. British Columbia, Vancouver, Tech. Rep. TR-2007-20 …, 2007	89	2007
New insights into one-norm solvers from the Pareto curve G Hennenfent, E van den Berg, MP Friedlander, FJ Herrmann Geophysics 73 (4), A23-A26, 2008	83	2008
Gauge optimization and duality MP Friedlander, I Macedo, TK Pong SIAM Journal on Optimization 24 (4), 1999-2022, 2014	80	2014

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