1 citations · 1 across the 7 of their papers we have counts for
7 papers
A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
We propose a hybrid reinforcement and self-supervised learning framework for accelerating generalized Benders decomposition (GBD). In this framework, a graph based reinforcement le…
Learning interpretable and stable dynamical models via mixed-integer Lyapunov-constrained optimization
Zhe Li, Ilias Mitrai
In this paper, we consider the data-driven discovery of stable dynamical models with a single equilibrium. The proposed approach uses a basis-function parameterization of the diffe…
Feasibility-Aware Imitation Learning for Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
Mixed-integer optimization problems arise in a wide range of control applications. Benders decomposition is a widely used algorithm for solving such problems by decomposing them in…
Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural n…
Discovering interpretable piecewise nonlinear model predictive control laws via symbolic decision trees
Ilias Mitrai
In this paper, we propose symbolic decision trees as surrogate models for approximating model predictive control laws. The proposed approach learns simultaneously the partition of…
Learning to control inexact Benders decomposition via reinforcement learning
Zhe Li, Bernard T. Agyeman, Ilias Mitrai +1
Benders decomposition (BD), along with its generalized version (GBD), is a widely used algorithm for solving large-scale mixed-integer optimization problems that arise in the opera…