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2017 (v1)PublicationUploaded on: April 14, 2023
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2016 (v1)Publication
We introduce a new stochastic gradient algorithm, SAAGA, and investigate its employment for solving Stochastic MPC problems and multi-stage stochastic optimization programs in general. The method is particularly attractive for scenario-based formulations that involve a large number of scenarios, for which 'batch' formulations may become...
Uploaded on: April 14, 2023 -
2015 (v1)Publication
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Uploaded on: April 14, 2023 -
2015 (v1)Publication
In this paper, we combine optimal control theory and machine learning techniques to propose and solve an optimal control formulation of online learning from supervised examples, which are used to learn an unknown vector parameter modeling the relationship between the input examples and their outputs. We show some connections of the problem...
Uploaded on: April 14, 2023 -
November 23, 2023 (v1)Publication
In safety-critical applications that rely on the solution of an optimization problem, the certification of the optimization algorithm is of vital importance. Certification and suboptimality results are available for a wide range of optimization algorithms. However, a typical underlying assumption is that the operations performed by the...
Uploaded on: November 25, 2023 -
May 26, 2023 (v1)Publication
How to efficiently implement Model Predictive Control (MPC) in embedded systems is a topic that is attracting a lot of research recently, due to its impact in practical applications. Implementing MPC in industrial Programmable Logic Controllers (PLCs) is of particular interest due to their widespread prevalence in the industry in comparison...
Uploaded on: May 27, 2023