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Found 12 papers, 5 papers with code
Predictive stability filters for nonlinear dynamical systems affected by disturbances
Predictive safety filters provide a way of projecting potentially unsafe inputs onto the set of inputs that guarantee recursive state and input constraint satisfaction.
A Stiffness-Oriented Model Order Reduction Method for Low-Inertia Power Systems
The fast modes are then truncated in the rotated coordinate system to obtain a lower-order model with reduced stiffness.
Asynchronous Computation of Tube-based Model Predictive Control
Tube-based model predictive control (MPC) methods bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
PAGE-PG: A Simple and Loopless Variance-Reduced Policy Gradient Method with Probabilistic Gradient Estimation
A numerical evaluation confirms the competitive performance of our method on classical control tasks.
System Level Disturbance Reachable Sets and their Application to Tube-based MPC
Tube-based model predictive control (MPC) methods leverage tubes to bound deviations from a nominal trajectory due to uncertainties in order to ensure constraint satisfaction.
Convergence Analysis of Homotopy-SGD for non-convex optimization
In this work, we present a first-order stochastic algorithm based on a combination of homotopy methods and SGD, called Homotopy-Stochastic Gradient Descent (H-SGD), which finds interesting connections with some proposed heuristics in the literature, e. g. optimization by Gaussian continuation, training by diffusion, mollifying networks.
An Efficient Real-Time NMPC for Quadrotor Position Control under Communication Time-Delay
The advances in computer processor technology have enabled the application of nonlinear model predictive control (NMPC) to agile systems, such as quadrotors.
Robotics Systems and Control Systems and Control Optimization and Control
On the Promise of the Stochastic Generalized Gauss-Newton Method for Training DNNs
This enables researchers to further study and improve this promising optimization technique and hopefully reconsider stochastic second-order methods as competitive optimization techniques for training DNNs; we also hope that the promise of SGN may lead to forward automatic differentiation being added to Tensorflow or Pytorch.
Transferring Optimality Across Data Distributions via Homotopy Methods
Homotopy methods, also known as continuation methods, are a powerful mathematical tool to efficiently solve various problems in numerical analysis, including complex non-convex optimization problems where no or only little prior knowledge regarding the localization of the solutions is available.
acados: a modular open-source framework for fast embedded optimal control
The acados software package is a collection of solvers for fast embedded optimization, intended for fast embedded applications.
Optimization and Control
The BLAS API of BLASFEO: optimizing performance for small matrices
This BLAS API has lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache.
Mathematical Software
BLASFEO: basic linear algebra subroutines for embedded optimization
BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization.
Mathematical Software
