4 papers
Inverse Optimization Latent Variable Models for Learning Costs Applied to Route Problems
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning representations for solutions of constrained optimization problems (COPs) with unknown cost functions is challenging, as models like (Variational) Autoencoders struggle to…
ZipMPC: Compressed Context-Dependent MPC Cost via Imitation Learning
Rahel Rickenbach, Alan A. Lahoud, Erik Schaffernicht +2
The computational burden of model predictive control (MPC) limits its application on real-time systems, such as robots, and often requires the use of short prediction horizons. Thi…
Learning Solutions of Stochastic Optimization Problems with Bayesian Neural Networks
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Mathematical solvers use parametrized Optimization Problems (OPs) as inputs to yield optimal decisions. In many real-world settings, some of these parameters are unknown or uncerta…
DataSP: A Differential All-to-All Shortest Path Algorithm for Learning Costs and Predicting Paths with Context
Alan A. Lahoud, Erik Schaffernicht, Johannes A. Stork
Learning latent costs of transitions on graphs from trajectories demonstrations under various contextual features is challenging but useful for path planning. Yet, existing methods…