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Found 15 papers, 6 papers with code
Data-Driven Performance Guarantees for Classical and Learned Optimizers
We build generalization guarantees for classical optimizers, using a sample convergence bound, and for learned optimizers, using the Probably Approximately Correct (PAC)-Bayes framework.
A neural network-based approach to hybrid systems identification for control
We consider the problem of designing a machine learning-based model of an unknown dynamical system from a finite number of (state-input)-successor state data points, such that the model obtained is also suitable for optimal control design.
Who Plays First? Optimizing the Order of Play in Stackelberg Games with Many Robots
To solve the problem, we introduce Branch and Play (B&P), an efficient and exact algorithm that provably converges to a socially optimal order of play and its Stackelberg equilibrium.
Differentiable Cutting-plane Layers for Mixed-integer Linear Optimization
We consider the problem of solving a family of parametric mixed-integer linear optimization problems where some entries in the input data change.
Learning to Warm-Start Fixed-Point Optimization Algorithms
We introduce a machine-learning framework to warm-start fixed-point optimization algorithms.
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies
Reinforcement learning (RL) for continuous control typically employs distributions whose support covers the entire action space.
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.
Exterior-point Optimization for Sparse and Low-rank Optimization
A wide range of nonconvex optimization problems have this structure including (but not limited to) sparse and low-rank optimization problems.
Optimization and Control
CoCo: Learning Strategies for Online Mixed-Integer Control
Mixed-integer convex programming (MICP) is a popular modeling framework for solving discrete and combinatorial optimization problems arising in various settings.
From predictions to prescriptions: A data-driven response to COVID-19
Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
Learning Convex Optimization Control Policies
Common examples of such convex optimization control policies (COCPs) include the linear quadratic regulator (LQR), convex model predictive control (MPC), and convex control-Lyapunov or approximate dynamic programming (ADP) policies.
Online Mixed-Integer Optimization in Milliseconds
Compared to state-of-the-art MIO routines, the online running time of our method is very predictable and can be lower than a single matrix factorization time.
The Voice of Optimization
We introduce the idea that using optimal classification trees (OCTs) and optimal classification trees with-hyperplanes (OCT-Hs), interpretable machine learning algorithms developed by Bertsimas and Dunn [2017, 2018], we are able to obtain insight on the strategy behind the optimal solution in continuous and mixed-integer convex optimization problem as a function of key parameters that affect the problem.
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
High-Speed Finite Control Set Model Predictive Control for Power Electronics
To the authors' knowledge, this is the first time direct MPC for current control has been implemented on an FPGA solving the integer optimization problem in real-time and achieving comparable performance to formulations with long prediction horizons.
Optimization and Control
