Tuesday, 13:15 - 13:40 h, Room: H 0112


Mihai Anitescu
Scalable dynamic optimization

Coauthor: Victor Zavala


In this talk, we discuss scalability issues arising in dynamic optimization problems such as model predictive control and data assimilation. We present potential strategies to avoid them, where we focus on scalable algorithms for methods that can track the optimal manifold with even one quadratic program per step. This builds on recent work of the authors where we proved using a generalized equations framework that such methods stabilize model predictive control formulation even when they have explicit inequality constraints. In particular, we present alternatives to enable fast active-set detection and matrix-free implementations.


Talk 1 of the invited session Tue.2.H 0112
"Real-time optimization II" [...]
Cluster 16
"Nonlinear programming" [...]


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