Print Email Facebook Twitter Optimize the indescribable Title Optimize the indescribable: A Look at the Unification between Machine Learning and Optimization Author Dijkstra, Finn (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Postek, K.S. (mentor) Nane, G.F. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science Date 2022-07-22 Abstract Packages to encode Machine Learned models into optimization problems is an underdeveloped area, despite the advantages is could provide. The main draw of implementing Machine Learned models into optimization models, is that it allows the optimizer to better account for the human experience.Maragno D., Wiberg H. et al. constructed an implementation of the encoding with their package OptiCL. In order to verify their implementation and provide principles for (re)designing packages with similar functions, an amount of components of OptiCL were replicated within this paper. The requirements forthe program were first constructed before detailing the implementation process. After the program was implemented, both OptiCL and the found program were tested in order to compare performances. Using the results and an investigation of the two implementations, a framework for encoding similar packageswas provided using the insights gained. Using mathematical formulations supplied by Maragno D., Wiberg H. et al., design principles outlined in this report and research into the encoding of other Machine Learned models, other developers could construct robust packages that allow for easy integration ofvaluable information gained from Machine Learning into optimization problems. This in turn allows for frequently used optimization models to account for more human understanding. Subject optimisationMachine learningConstraintlinear programming To reference this document use: http://resolver.tudelft.nl/uuid:729a5979-9f1e-4baa-b29e-111455a1e0c3 Part of collection Student theses Document type bachelor thesis Rights © 2022 Finn Dijkstra Files PDF Thesis_Finn_OptimizeTheIn ... ibable.pdf 6.91 MB Close viewer /islandora/object/uuid:729a5979-9f1e-4baa-b29e-111455a1e0c3/datastream/OBJ/view