Standard contrastive learning approaches usually require a large number of negatives for
effective unsupervised learning and often exhibit slow convergence. We suspect this …

Appropriate battery storage capacity plays an important role in the performance and cost of
residential energy systems. However, the load demand and renewable energy generation …

We present ReLU-QP, a GPU-accelerated solver for quadratic programs (QPs) that is
capable of solving high-dimensional control problems at real-time rates. ReLU-QP is derived …

Nonlinear Model Predictive Control (NMPC) is a state-of-the-art approach for locomotion
and manipulation which leverages trajectory optimization at each control step. While the …

Numerical algorithms to solve mathematical optimization problems efficiently are essential to
applications in many areas of engineering and computational science. To solve optimization …

The interior-point method (IPM) has become the workhorse method for nonlinear
programming. The performance of IPM is directly related to the linear solver employed to …

Applications in robotics or other size-, weight-, and power-constrained (SWaP) autonomous
systems at the edge often require real-time and low-energy solutions to large optimization …

We introduce a GPU-accelerated Monte Carlo framework for nonconvex, free-final-time
trajectory optimization problems. This framework utilizes the prox-linear method, which …

This paper investigates the reliable shortest path (RSP) problem in Gaussian process (GP)
regulated transportation networks. Specifically, the RSP problem that we are targeting at is …

Recent advances in neural-network architecture allow for seamless integration of convex
optimization problems as differentiable layers in an end-to-end trainable neural network …