1 citations · 2 across the 14 of their papers we have counts for
18 papers
Stochastic Sequential Feedback Optimization for Wind Farm Control under Time-Varying Inflow
Shijie Huang, Sergio Grammatico
We consider steady-state optimization of nonlinear dynamical systems under time-varying stochastic operating conditions, where the objective is an expected-value function over the…
Learning in Inverse Games: Tractable Training with Probabilistic Guarantees
Arghya Mallick, Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani +1
Inverse game theory seeks to learn agents' unknown objectives from observed equilibrium behavior. Existing residual-based approaches can lead to non-convex problems and need not en…
Hybrid Sequential Feedback Optimization for Wind Farm Power Maximization
Shijie Huang, Sergio Grammatico
This paper considers feedback optimization for optimal steady-state operation of nonlinear discrete-time systems when the steady-state input-output map and its sensitivity are expe…
Adaptive Incentive Design in Dynamic Principal-Agent Problem via Kernelized Bandits
Arghya Mallick, Anuj S. Vora, Sergio Grammatico +1
We consider the dynamic principal-agent problem under asymmetric information, wherein a principal sequentially designs contracts to incentivize an agent with unknown preferences an…
Benchmarking Sequential Feedback Optimization for Wind Farm Power Maximization
Shijie Huang, Sergio Grammatico
This paper benchmarks sequential feedback optimization (SFO) for wind farm power maximization using a medium-fidelity dynamic flow model. We compare SFO with two well-established a…
Fast Newton methods for linear-quadratic dynamic games with application to autonomous vehicle platooning and intersection crossing
Reza Rahimi Baghbadorani, Sergio Grammatico
We consider constrained linear-quadratic dynamic games arising in autonomous vehicle platooning, intersection crossing and other cooperative driving scenarios. Infinite-horizon Nas…
Learning-Based Stackelberg Equilibrium Seeking with Application to Demand-Side Energy Management
Silvia Cianchi, Reza Rahimi Baghbadorani, Anibal Sanjab +1
Demand-side management (DSM) enables distribution system operators (DSOs) to steer electricity consumption through dynamic price signals or incentive mechanisms, thereby leveraging…
Induced Stackelberg Equilibrium Seeking via Iterative Tikhonov Regularization
Silvia Cianchi, Anibal Sanjab, Sergio Grammatico
Existing methods for learning Stackelberg equilibria typically assume that the followers' (variational, generalized) Nash equilibrium is unique. However, in the presence of multipl…
A Hybrid Algorithm for Monotone Variational Inequalities
Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani, Sergio Grammatico
Inspired by the adaptive Golden Ratio Algorithm (aGRAAL), we propose two new methods for solving monotone variational inequalities. We show that by selecting the momentum parameter…
Locally Linear Convergence for Nonsmooth Convex Optimization via Coupled Smoothing and Momentum
Reza Rahimi Baghbadorani, Sergio Grammatico, Peyman Mohajerin Esfahani
We propose an adaptive accelerated smoothing technique for a nonsmooth convex optimization problem where the smoothing update rule is coupled with the momentum parameter. We also e…
Adversarially and Distributionally Robust Virtual Energy Storage Systems via the Scenario Approach
Georgios Pantazis, Nicola Mignoni, Raffaele Carli +2
We study virtual energy storage services based on the aggregation of EV batteries in parking lots under time-varying, uncertain EV departures and state-of-charge limits. We propose…
A Frank-Wolfe Algorithm for Strongly Monotone Variational Inequalities
Reza Rahimi Baghbadorani, Peyman Mohajerin Esfahani, Sergio Grammatico
We propose an accelerated algorithm with a Frank-Wolfe method as an oracle for solving strongly monotone variational inequality problems. While standard solution approaches, such a…
Sequential feedback optimization with application to wind farm control
Shijie Huang, Sergio Grammatico
This paper develops a sequential-linearization feedback optimization framework for driving nonlinear dynamical systems to an optimal steady state. A fundamental challenge in feedba…
User-centric Vehicle-to-Grid Optimization with an Input Convex Neural Network-based Battery Degradation Model
Arghya Mallick, Georgios Pantazis, Mohammad Khosravi +2
We propose a data-driven, user-centric vehicle-to-grid (V2G) methodology based on multi-objective optimization to balance battery degradation and V2G revenue according to EV user p…
A User-centric Game for Balancing V2G Benefits with Battery Degradation of Electric Vehicles
Arghya Mallick, Georgios Pantazis, Peyman Mohajerin Esfahani +1
We present a novel user-centric vehicle-to-grid (V2G) framework that enables electric vehicle (EV) users to balance the trade-off between financial benefits from V2G and battery he…
A Douglas-Rachford Splitting Method for Solving Monotone Variational Inequalities in Linear-quadratic Dynamic Games
Reza Rahimi Baghbadorani, Emilio Benenati, Sergio Grammatico
This paper considers constrained linear dynamic games with quadratic objective functions, which can be cast as affine variational inequalities. By leveraging the problem structure,…
A New Lineserach for Accelerated Composite Minimization
Reza Rahimi Baghbadorani, Sergio Grammatico, Peyman Mohajerin Esfahani
The choice of the stepsize in first-order convex optimization is typically based on the smoothness constant and plays a crucial role in the performance of algorithms. Recently, the…
Estimation Network Design framework for efficient distributed optimization
Mattia Bianchi, Sergio Grammatico
Distributed decision problems features a group of agents that can only communicate over a peer-to-peer network, without a central memory. In applications such as network control an…