Approximate Optimization Professors in Global

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Vijaya Raghavendra Bollapragada
University of Texas at Austin United States of America
optimization algorithms
stochastic optimization
constrained optimization
nonlinear optimization
distributed optimization
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Kalyanmoy Deb
Michigan State University United States of America
process optimization
computational optimization
multi-objective optimization
hybrid optimization algorithms
evolutionary multi-objective optimization
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Albert Berahas
University of Michigan United States of America
stochastic optimization
constrained optimization
machine learning optimization
nonlinear optimization
distributed optimization
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Retsef Levi
Massachusetts Institute of Technology United States of America
approximation algorithms
optimization
stochastic optimization
combinatorial optimization
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Venkat Chandrasekaran
California Institute of Technology United States of America
optimization
approximation theory
combinatorial optimization
convex optimization
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Cynthia Vinzant
University of Washington United States of America
mathematical optimization
linear optimization
discrete optimization
combinatorial optimization
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Michael Patriksson
University of Gothenburg Sweden
mathematical optimization
nonlinear optimization
combinatorial optimization
convex optimization
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Bartolomeo Stellato
Princeton University United States of America
mathematical optimization
large scale optimization
real-time embedded optimization
differentiable optimization
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Shixuan Zhang
Texas A&M University United States of America
stochastic optimization
polynomial optimization
integer optimization
robust optimization
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Nisheeth Vishnoi
Yale University United States of America
approximation algorithms
non-convex optimization
combinatorial optimization
convex optimization
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John Chinneck
Carleton University Canada
optimization
feasibility optimization
nonlinear optimization
mixed integer optimization
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Dick Den Hertog
University of Amsterdam Netherlands
linear optimization
optimization in machine learning
nonlinear optimization
robust optimization
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Eric Kerrigan
Imperial College London United Kingdom
multi-objective optimization
dynamic optimization
control optimization
numerical optimization methods
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Yaoliang Yu
University of Waterloo Canada
optimization algorithms
non-convex optimization
robust stochastic optimization
convex optimization
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Ruth Misener
Imperial College London United Kingdom
computational optimization
optimization models
data-driven optimization
robust optimization
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Mahdi Mehrtash
Johns Hopkins University United States of America
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stochastic optimization
distributed optimization
global optimization
power system optimization
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Mehrdad Mahdavi
Pennsylvania State University United States of America
large-scale optimization
non-convex optimization
distributed optimization
convex optimization
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Michael Patriksson
Chalmers University of Technology Sweden
nonlinear optimization
combinatorial optimization
convex optimization
non-differentiable optimization
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Jelena Diakonikolas
University of Wisconsin–Madison United States of America
large-scale optimization
non-convex optimization
convex optimization
first-order optimization algorithms
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Merve Bodur
University of Edinburgh United Kingdom
combinatorial optimization
inverse optimization
multiobjective optimization
robust optimization
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Merve Bodur
University of Edinburgh United Kingdom
combinatorial optimization
inverse optimization
multiobjective optimization
robust optimization
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