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Research in LIDS focuses on efficient and scalable algorithms for large scale problems
Optimization is a core methodological discipline that aims to develop analytical and computational methods for solving optimization problems in engineering, data science, and operations research. Research in LIDS focuses on efficient and scalable algorithms for large scale problems, their theoretical understanding, and the deployment of modern optimization techniques to challenging settings in diverse applications ranging from communication networks and power systems to machine learning.
In addition, there is a natural overlap between optimization and control, as much of modern control theory rests on optimization formulations. Finally the increased interest in systems that involve simultaneous optimization by several, possibly competing agents has led to several research thrusts that rely on game-theoretic approaches.
In addition, there is a natural overlap between optimization and control, as much of modern control theory rests on optimization formulations. Finally the increased interest in systems that involve simultaneous optimization by several, possibly competing agents has led to several research thrusts that rely on game-theoretic approaches.
Distributed nonlinear optimization algorithms
Optimization methods for supervised learning
Optimization methods that rely on algebraic techniques
Optimization in the power grid
Reinforcement learning for stochastic optimal control
Stochastic gradient descent algorithms and their analysis
Cyber-physical systems: design, security, algorithms, analysis, and verification.
Design of incentives and mechanisms in networked, dynamic environments
New equilibrium notions and dynamics in games
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Laboratory for Information and Decision Systems
Massachusetts Institute of Technology
Room 32-D60
877 Massachusetts Avenue
Cambridge, MA 02139
Massachusetts Institute of Technology
Room 32-D60
877 Massachusetts Avenue
Cambridge, MA 02139
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