James Luedtke

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Jeff LinderothUniversity of Wisconsin-Madison. Wisconsin Institute for DiscoveryVerified email at wisc.edu
Shabbir AhmedProfessor of Industrial & Systems Engineering, Georgia Institute of TechnologyVerified email at isye.gatech.edu
Sven LeyfferSenior Computational Mathematician, Argonne National LaboratoryVerified email at anl.gov
Ashutosh MahajanAssistant Professor, Industrial Engineering and Operations Research, IIT BombayVerified email at iitb.ac.in
Merve BodurUniversity of TorontoVerified email at mie.utoronto.ca
Yongjia SongClemson UniversityVerified email at clemson.edu
George NemhauserProfessor, Georgia Institute of TechnologyVerified email at isye.gatech.edu
Pietro BelottiPolitecnico di MilanoVerified email at polimi.it
Christian KirchesProfessor, Technische Universität Carolo-Wilhelmina zu BraunschweigVerified email at tu-bs.de
Rohit KannanLos Alamos National LaboratoryVerified email at lanl.gov
Simge KüçükyavuzNorthwestern UniversityVerified email at northwestern.edu
Srikrishna SridharAppleVerified email at tetra.ai
Mustafa KilincExxonMobilVerified email at exxonmobil.com
Sanjeeb DashIBM ResearchVerified email at us.ibm.com
Itai GurvichNorthwestern University, Kellogog School of ManagementVerified email at kellogg.northwestern.edu
Tolga TezcanLondon Business SchoolVerified email at london.edu
Todd MunsonArgonne National LaboratoryVerified email at mcs.anl.gov
Claudia D'AmbrosioCNRS researcher, LIX Ecole PolytechniqueVerified email at lix.polytechnique.fr
Güzin BayraksanProfessor, the Ohio State UniversityVerified email at osu.edu
Eli TowleSenior Optimization Engineer, Gurobi OptimizationVerified email at gurobi.com

James Luedtke

Professor, Ind. & Sys. Engr and Wisconsin Institute for Discovery, UW-Madison

Verified email at wisc.edu - Homepage

Optimization Stochastic programming Mixed-Integer Programming


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Mixed-integer nonlinear optimization P Belotti, C Kirches, S Leyffer, J Linderoth, J Luedtke, A Mahajan Acta Numerica 22, 1-131, 2013	753	2013
A sample approximation approach for optimization with probabilistic constraints J Luedtke, S Ahmed SIAM Journal on Optimization 19 (2), 674-699, 2008	644	2008
An integer programming approach for linear programs with probabilistic constraints J Luedtke, S Ahmed, GL Nemhauser Mathematical programming 122 (2), 247-272, 2010	383	2010
A branch-and-cut decomposition algorithm for solving chance-constrained mathematical programs with finite support J Luedtke Mathematical Programming 146, 219-244, 2014	194	2014
Staffing call centers with uncertain demand forecasts: A chance-constrained optimization approach I Gurvich, J Luedtke, T Tezcan Management Science 56 (7), 1093-1115, 2010	139	2010
New formulations for optimization under stochastic dominance constraints J Luedtke SIAM Journal on Optimization 19 (3), 1433-1450, 2008	112	2008
Decomposition Algorithms for Two-Stage Chance-Constrained Programs X Liu, S Küçükyavuz, J Luedtke Mathematical Programming, 2014	100	2014
Chance-constrained binary packing problems Y Song, J Luedtke, S Küçükyavuz INFORMS Journal on Computing 26, 735-747, 2014	97	2014
Some Results on the Strength of Relaxations of Multilinear Functions J Luedtke, M Namazifar, J Linderoth Mathematical Programming 136, 325-351, 2012	96	2012
Strengthened Benders cuts for stochastic integer programs with continuous recourse M Bodur, S Dash, O Gunluk, J Luedtke INFORMS Journal on Computing 29 (1), 77-91, 2017	84	2017
Lift-and-project cuts for convex mixed integer nonlinear programs: Linear programming based separation and extended formulations MR Kılınç, J Linderoth, J Luedtke Mathematical Programming Computation 9, 499-526, 2017	70*	2017
Mixed-integer rounding enhanced benders decomposition for multiclass service system staffing and scheduling with arrival rate uncertainty M Bodur, J Luedtke Management Science, 2016	67	2016
A cycle-based formulation and valid inequalities for DC power transmission problems with switching B Kocuk, H Jeon, SS Dey, J Linderoth, J Luedtke, A Sun Operations Research, 2016	62	2016
Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs S Ahmed, J Luedtke, Y Song, W Xie Mathematical Programming, 2016	61	2016
Applications and algorithms for mixed integer nonlinear programming S Leyffer, J Linderoth, J Luedtke, A Miller, T Munson Journal of Physics: Conference Series 180 (1), 012014, 2009	61	2009
Combining progressive hedging with a frank-wolfe method to compute lagrangian dual bounds in stochastic mixed-integer programming N Boland, J Christiansen, B Dandurand, A Eberhard, J Linderoth, ... SIAM Journal on Optimization, 2017	57	2017
Exact algorithms for the chance-constrained vehicle routing problem T Dinh, R Fukasawa, J Luedtke Mathematical Programming 172 (1), 105-138, 2018	56	2018
An Adaptive Partition-based Approach for Solving Two-stage Stochastic Programs with Fixed Recourse Y Song, J Luedtke SIAM Journal on Optimization 25 (3), 1344-1367, 2014	51	2014
Solving chance-constrained problems via a smooth sample-based nonlinear approximation A Peña-Ordieres, JR Luedtke, A Wächter SIAM Journal on Optimization 30 (3), 2221-2250, 2020	46	2020
Locally ideal formulations for piecewise linear functions with indicator variables S Sridhar, J Linderoth, J Luedtke Operations Research Letters 41 (6), 627-632, 2013	46	2013

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