We introduce a machine-learning framework to learn the hyperparameter sequence of first-
order methods (eg, the step sizes in gradient descent) to quickly solve parametric convex …

Learning-to-optimize (L2O) is an emerging research area in large-scale optimization for data
science applications. Very recently, researchers have proposed a novel L2O framework …

Traditional network management algorithms have relied on prior knowledge of system
models and networking scenarios. In practice, a universal optimization framework is …

There has been growing interest in employing neural networks (NNs) to directly solve
constrained optimization problems with low run-time complexity. However, it is non-trivial to …

The rapid deployment of robotics technologies requires dedicated optimization algorithms to
manage large fleets of autonomous agents. This paper supports robotic parts-to-picker …

We introduce a numerical framework to verify the finite step convergence of first-order
methods for parametric convex quadratic optimization. We formulate the verification problem …

Quadratic programming (QP) forms a crucial foundation in optimization, encompassing a
broad spectrum of domains and serving as the basis for more advanced algorithms …

We present a dynamic position and orientation controller for a hybrid rigid-soft manipulator
arm where the soft arm is extruded from a two degrees-of-freedom rigid link. Our approach …

Diffusion models, which iteratively denoise data samples to synthesize high-quality outputs,
have achieved empirical success across domains. However, optimizing these models for …

In recent years, many deep learning approaches have incorporated layers that solve
optimization problems (eg, linear, quadratic, and semidefinite programs). Integrating these …