FORCES NLP: An efficient implementation of interior-point methods for multistage nonlinear nonconvex programs
Real-time implementation of optimisation-based control and trajectory planning can be very
challenging for nonlinear systems. As a result, if an implementation based on a fixed …
challenging for nonlinear systems. As a result, if an implementation based on a fixed …
Using stochastic programming to train neural network approximation of nonlinear MPC laws
To facilitate the real-time implementation of nonlinear model predictive control (NMPC), this
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …
paper proposes a deep learning-based NMPC scheme, in which the NMPC law is …
Scalable parallel nonlinear optimization with PyNumero and Parapint
JS Rodriguez, RB Parker, CD Laird… - INFORMS Journal …, 2023 - pubsonline.informs.org
We describe PyNumero, an open-source, object-oriented programming framework in Python
that supports rapid development of performant parallel algorithms for structured nonlinear …
that supports rapid development of performant parallel algorithms for structured nonlinear …
Optimal decomposition for distributed optimization in nonlinear model predictive control through community detection
Distributed optimization, based on a decomposition of the entire optimization problem, has
been applied to many complex decision making problems in process systems engineering …
been applied to many complex decision making problems in process systems engineering …
Decomposition of nonconvex optimization via bi-level distributed ALADIN
Decentralized optimization algorithms are of interest in different contexts, eg, optimal power
flow or distributed model predictive control, as they avoid central coordination and enable …
flow or distributed model predictive control, as they avoid central coordination and enable …
Utilizing modern computer architectures to solve mathematical optimization problems: A survey
Numerical algorithms to solve mathematical optimization problems efficiently are essential to
applications in many areas of engineering and computational science. To solve optimization …
applications in many areas of engineering and computational science. To solve optimization …
Distributed control and optimization of process system networks: A review and perspective
W Tang, P Daoutidis - Chinese Journal of Chemical Engineering, 2019 - Elsevier
Large-scale and complex process systems are essentially interconnected networks. The
automated operation of such process networks requires the solution of control and …
automated operation of such process networks requires the solution of control and …
Decomposition of control and optimization problems by network structure: Concepts, methods, and inspirations from biology.
First, we point out that available decomposition-based control and optimization algorithms
are essentially based on some I block structure i in the underlying I network i topology of the …
are essentially based on some I block structure i in the underlying I network i topology of the …
Minimization of energy consumption in multi-stage evaporator system of Kraft recovery process using Interior-Point Method
The maximization of the energy (steam) efficiency of a multi-stage evaporator system used
for concentrating the black liquor in pulp and paper mills carries immense significance in …
for concentrating the black liquor in pulp and paper mills carries immense significance in …
A graph-based modeling abstraction for optimization: Concepts and implementation in plasmo. jl
We present a general graph-based modeling abstraction for optimization that we call an
OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical …
OptiGraph. Under this abstraction, any optimization problem is treated as a hierarchical …