5 papers
A Differentiable Interior-Point Method in Single Precision
Jon Arrizabalaga, Kevin Tracy, Zachary Manchester
Primal-dual interior-point methods solve constrained convex optimization problems to tight tolerances with speed and robustness. Their solutions are also efficiently differentiable…
TinySDP: Real Time Semidefinite Optimization for Certifiable and Agile Edge Robotics
Ishaan Mahajan, Jon Arrizabalaga, Andrea Grillo +4
Semidefinite programming (SDP) provides a principled framework for convex relaxations of nonconvex geometric constraints in motion planning, yet existing solvers are too computatio…
Implicit Primal-Dual Interior-Point Methods for Quadratic Programming
Jon Arrizabalaga, Zachary Manchester
This paper introduces a new method for solving quadratic programs using primal-dual interior-point methods. Instead of handling complementarity as an explicit equation in the Karus…
Convex Maneuver Planning for Spacecraft Collision Avoidance
Fausto Vega, Jon Arrizabalaga, Ryan Watson +1
Conjunction analysis and maneuver planning for spacecraft collision avoidance remains a manual and time-consuming process, typically involving repeated forward simulations of hand-…
The Trajectory Bundle Method: Unifying Sequential-Convex Programming and Sampling-Based Trajectory Optimization
Kevin Tracy, John Z. Zhang, Jon Arrizabalaga +4
We present a unified framework for solving trajectory optimization problems in a derivative-free manner through the use of sequential convex programming. Traditionally, nonconvex o…