A dual gradient-projection algorithm for model predictive control in fixed-point arithmetic

Automatica ◽  
2015 ◽  
Vol 55 ◽  
pp. 226-235 ◽  
Author(s):  
Panagiotis Patrinos ◽  
Alberto Guiggiani ◽  
Alberto Bemporad
Keyword(s):  
Fixed Point ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Fixed Point Arithmetic ◽  
Gradient Projection Algorithm ◽  
Dual Gradient ◽  
Point Arithmetic

scholarly journals Relaxed gradient projection algorithm for constrained node-based shape optimization

2021 ◽  
Author(s):  
Ihar Antonau ◽  
Majid Hojjat ◽  
Kai-Uwe Bletzinger
Keyword(s):  
Shape Optimization ◽  
Optimization Problems ◽  
Gradient Projection ◽  
Projection Algorithm ◽  
Design Parameters ◽  
Active Set ◽  
Physical Constraints ◽  
Original Algorithm ◽  
Gradient Projection Algorithm ◽  
Non Linear

AbstractIn node-based shape optimization, there are a vast amount of design parameters, and the objectives, as well as the physical constraints, are non-linear in state and design. Robust optimization algorithms are required. The methods of feasible directions are widely used in practical optimization problems and know to be quite robust. A subclass of these methods is the gradient projection method. It is an active-set method, it can be used with equality and non-equality constraints, and it has gained significant popularity for its intuitive implementation. One significant issue around efficiency is that the algorithm may suffer from zigzagging behavior while it follows non-linear design boundaries. In this work, we propose a modification to Rosen’s gradient projection algorithm. It includes the efficient techniques to damp the zigzagging behavior of the original algorithm while following the non-linear design boundaries, thus improving the performance of the method.

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