Sustainability at LIDS...
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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
