13 papers
Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates
Anjian Li, Ryne Beeson
Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are otherwise numerically expensive t…
GLENS: Global Search via Learning from Solver Iterates with Diffusion Models
Anjian Li, Bartolomeo Stellato, Ryne Beeson
We consider the problem of generating a large collection of initial guesses for local minima of multimodal non-convex continuous optimization problems. The goal is for these initia…
Transfer Learning of Multiobjective Indirect Low-Thrust Trajectories Using Diffusion Models and Markov Chain Monte Carlo
Jannik Graebner, Ryne Beeson
Preliminary low-thrust spacecraft mission design is a global search problem characterized by a complex solution landscape, multiple objectives, and numerous local minima. During th…
Bi-Level Optimal Control Framework For Missed-Thrust-Design With First-Order Bounds On Maximum Missed-Thrust-Duration
Amlan Sinha, Ryne Beeson
In this paper, we present a bi-level optimal control framework for designing low-thrust spacecraft trajectories with robustness against missed-thrust-events. The upper-level (UL) p…
Gradient-Informed Monte Carlo Fine-Tuning of Diffusion Models for Low-Thrust Trajectory Design
Jannik Graebner, Ryne Beeson
Preliminary mission design of low-thrust spacecraft trajectories in the Circular Restricted Three-Body Problem is a global search characterized by a complex objective landscape and…
Self-supervised diffusion model fine-tuning for costate initialization using Markov chain Monte Carlo
Jannik Graebner, Ryne Beeson
Global search and optimization of long-duration, low-thrust spacecraft trajectories with the indirect method is challenging due to a complex solution space and the difficulty of ge…