13 citations · 23 across the 13 of their papers we have counts for
13 papers
Approximate Dynamic Optimization via Deep Neural Operators
Amin Nassaji, Ilias Mitrai, Prodromos Daoutidis
This paper addresses the solution of nonlinear dynamic optimization problems that compute optimal manipulated input profiles to enforce desired output profiles. Such trajectory opt…
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…
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…
Integer L-Shaped Method with Non-Supporting No-Good Optimality Cuts
Benjamin P. Riley, Prodromos Daoutidis, Qi Zhang
Two-stage stochastic mixed-integer linear programs with mixed-integer recourse arise in many practical applications but are computationally challenging due to their large size and…
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…
Topological descriptors for the electron density of inorganic solids
Nathan J. Szymanski, Alexander Smith, Prodromos Daoutidis +1
Descriptors play an important role in data-driven materials design. While most descriptors of crystalline materials emphasize structure and composition, they often neglect the elec…
Accelerating process control and optimization via machine learning: A review
Ilias Mitrai, Prodromos Daoutidis
Process control and optimization have been widely used to solve decision-making problems in chemical engineering applications. However, identifying and tuning the best solution alg…
Taking the human out of decomposition-based optimization via artificial intelligence: Part II. Learning to initialize
Ilias Mitrai, Prodromos Daoutidis
The repeated solution of large-scale optimization problems arises frequently in process systems engineering tasks. Decomposition-based solution methods have been widely used to red…
Taking the human out of decomposition-based optimization via artificial intelligence: Part I. Learning when to decompose
Ilias Mitrai, Prodromos Daoutidis
In this paper, we propose a graph classification approach for automatically determining whether to use a monolithic or a decomposition-based solution method. In this approach, an o…
Computationally efficient solution of mixed integer model predictive control problems via machine learning aided Benders Decomposition
Ilias Mitrai, Prodromos Daoutidis
Mixed integer Model Predictive Control (MPC) problems arise in the operation of systems where discrete and continuous decisions must be taken simultaneously to compensate for distu…
Automated Characterization and Monitoring of Material Shape using Riemannian Geometry
Alexander Smith, Steven Schilling, Prodromos Daoutidis
Shape affects both the physical and chemical properties of a material. Characterizing the roughness, convexity, and general geometry of a material can yield information on its cata…
Fast and Stable Nonconvex Constrained Distributed Optimization: The ELLADA Algorithm
Wentao Tang, Prodromos Daoutidis
Distributed optimization, where the computations are performed in a localized and coordinated manner using multiple agents, is a promising approach for solving large-scale optimiza…