An alternative complementarity formulation for primal-dual interior-point methods keeps linear systems spectrally bounded near the solution, enabling stable single-precision solves and differentiation for bilevel and end-to-end learning.
On the differentiability of the primal-dual interior-point method
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GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.
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GATO: GPU-Accelerated and Batched Trajectory Optimization for Scalable Edge Model Predictive Control
GATO is a new batched GPU trajectory optimization solver that achieves real-time MPC throughput with 18-21x speedups over CPU baselines for tens to low-hundreds of simultaneous solves.