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Found 7 papers, 3 papers with code
Online Computation of Terminal Ingredients in Distributed Model Predictive Control for Reference Tracking
A distributed model predictive control scheme is developed for tracking piecewise constant references where the terminal set is reconfigured online, whereas the terminal controller is computed offline.
Stochastic convex optimization for provably efficient apprenticeship learning
We consider large-scale Markov decision processes (MDPs) with an unknown cost function and employ stochastic convex optimization tools to address the problem of imitation learning, which consists of learning a policy from a finite set of expert demonstrations.
Efficient Performance Bounds for Primal-Dual Reinforcement Learning from Demonstrations
We consider large-scale Markov decision processes with an unknown cost function and address the problem of learning a policy from a finite set of expert demonstrations.
Accelerating Quadratic Optimization with Reinforcement Learning
First-order methods for quadratic optimization such as OSQP are widely used for large-scale machine learning and embedded optimal control, where many related problems must be rapidly solved.
Distributed Model Predictive Control with Reconfigurable Terminal Ingredients for Reference Tracking
The proposed scheme is tested in simulation where the proposed MPC problem is solved using distributed optimization.
GPU Acceleration of ADMM for Large-Scale Quadratic Programming
The alternating direction method of multipliers (ADMM) is a powerful operator splitting technique for solving structured convex optimization problems.
Optimization and Control
OSQP: An Operator Splitting Solver for Quadratic Programs
We present a general purpose solver for convex quadratic programs based on the alternating direction method of multipliers, employing a novel operator splitting technique that requires the solution of a quasi-definite linear system with the same coefficient matrix at almost every iteration.
Optimization and Control
