4 papers · 1 filter
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…
Learning Optimal Control and Dynamical Structure of Global Trajectory Search Problems with Diffusion Models
Jannik Graebner, Anjian Li, Amlan Sinha +1
Spacecraft trajectory design is a global search problem, where previous work has revealed specific solution structures that can be captured with data-driven methods. This paper exp…
Constraint-Aware Diffusion Models for Trajectory Optimization
Anjian Li, Zihan Ding, Adji Bousso Dieng +1
The diffusion model has shown success in generating high-quality and diverse solutions to trajectory optimization problems. However, diffusion models with neural networks inevitabl…