11 citations · 25 across the 10 of their papers we have counts for
11 papers · 1 filter
Inverse Optimization via Learning Feasible Regions
Ke Ren, Peyman Mohajerin Esfahani, Angelos Georghiou
We study inverse optimization (IO), where the goal is to use a parametric optimization program as the hypothesis class to infer relationships between input-decision pairs. Most of…
Mitigating optimistic bias in entropic risk estimation and optimization
Utsav Sadana, Erick Delage, Angelos Georghiou
The entropic risk measure is widely used in high-stakes decision-making across economics, management science, finance, and safety-critical control systems because it captures tail…
A Robust Optimization Approach to Network Control Using Local Information Exchange
Georgios Darivianakis, Angelos Georghiou, Soroosh Shafiee +1
Designing policies for a network of agents is typically done by formulating an optimization problem where each agent has access to state measurements of all the other agents in the…
Distributionally Robust Optimization with Decision-Dependent Information Discovery
Qing Jin, Angelos Georghiou, Phebe Vayanos +1
We study two-stage distributionally robust optimization (DRO) problems with decision-dependent information discovery (DDID) wherein (a portion of) the uncertain parameters are reve…
Robust Data-driven Prescriptiveness Optimization
Mehran Poursoltani, Erick Delage, Angelos Georghiou
The abundance of data has led to the emergence of a variety of optimization techniques that attempt to leverage available side information to provide more anticipative decisions. T…
Exact and Approximate Schemes for Robust Optimization Problems with Decision Dependent Information Discovery
Rosario Paradiso, Angelos Georghiou, Said Dabia +1
Uncertain optimization problems with decision dependent information discovery allow the decision maker to control the timing of information discovery, in contrast to the classic mu…
Robust Optimization with Decision-Dependent Information Discovery
Phebe Vayanos, Angelos Georghiou, Han Yu
Robust optimization is a popular paradigm for modeling and solving two- and multi-stage decision-making problems affected by uncertainty. In many real-world applications, the time…
Decentralized decision making for networks of uncertain systems
Georgios Darivianakis, Angelos Georghiou, John Lygeros
Distributed model predictive control (MPC) has been proven a successful method in regulating the operation of large-scale networks of constrained dynamical systems. This paper is c…
Low-complexity method for hybrid MPC with local guarantees
Damian Frick, Angelos Georghiou, Juan L. Jerez +2
Model predictive control problems for constrained hybrid systems are usually cast as mixed-integer optimization problems (MIP). However, commercial MIP solvers are designed to run…
The Power of Diversity: Data-Driven Robust Predictive Control for Energy Efficient Buildings and Districts
Georgios Darivianakis, Angelos Georghiou, Roy S. Smith +1
The cooperative energy management of aggregated buildings has recently received a great deal of interest due to substantial potential energy savings. These gains are mainly obtaine…
Robust Optimal Control with Adjustable Uncertainty Sets
Xiaojing Zhang, Maryam Kamgarpour, Angelos Georghiou +2
In this paper, we develop a unified framework for studying constrained robust optimal control problems with adjustable uncertainty sets. In contrast to standard constrained robust…