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Found 6 papers, 1 papers with code
MORBDD: Multiobjective Restricted Binary Decision Diagrams by Learning to Sparsify
We focus on binary decision diagrams (BDDs) which first construct a graph that represents all feasible solutions to the problem and then traverse the graph to extract the Pareto frontier.
BDD for Complete Characterization of a Safety Violation in Linear Systems with Inputs
But given higher requirements on control design, a designer is expected to satisfy other specification such as safety or temporal logic specification as well, and a naive control design might not satisfy such specification.
Constraint Learning to Define Trust Regions in Predictive-Model Embedded Optimization
In this paper, we provide an overview of the approaches appearing in the literature to construct a trust region, and propose three alternative approaches.
Optimizing over an ensemble of neural networks
We study how to incorporate a neural network ensemble as the objective function of an optimization model and explore computational approaches for the ensuing problem.
JANOS: An Integrated Predictive and Prescriptive Modeling Framework
The framework considers two sets of decision variables; regular and predicted.
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement Learning
Finding tight bounds on the optimal solution is a critical element of practical solution methods for discrete optimization problems.
