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Showing 1 to 7 of 7 for “"Bilevel optimisation"”.
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An analytics-based decomposition approach to large-scale bilevel optimisation
Bilevel optimisation problems contain several decision makers, each with different objectives and constraints, arranged in a hierarchical structure. One type of bilevel problem is the single-leader, multiple-follower problem, which has been used in applications like toll-setting, resource …
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Geometric numerical integration for optimisation
… we study geometric numerical integration for the optimisation of various classes of functionals. Numerical integration and the study of systems of differential equations have received increased attention within the optimisation community in the last decade, as a means for devising new optimisation …
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New PDE models for imaging problems and applications
… the two by using training sets of examples via bilevel optimisation. Numerically, we use a combination of SemiSmooth (SSN) and quasi-Newton methods to solve the problem efficiently. Finally, we consider TV-based models in the framework of graphs for image segmentation problems. Here, spectral …
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Set-based Particle Swarm Optimisation for Dynamic Optimisation Problems
Many real-world optimisation problems are inherently dynamic, defined by changes in their underlying properties over time. Real-world problems also frequently require optimisation over discrete-valued decision variables. However, the solution of problems that are simultaneously dynamic and …
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Models of neural circuits as optimally driven dynamical systems
… in a delayed reaching task as the objective of optimisation. Next, we propose a novel method to learn input-driven dynamical systems directly from data, this time optimising the inputs to yield the best possible description of the observations given a setting of the dynamics, and using a bilevel …
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Bridging Deep Learning and Probabilistic Inference: Towards Data Efficiency, Identifiability, and Sampling Scalability
… tasks. By formulating this problem in a novel bilevel optimisation framework and solving it with the implicit function theorem, this approach enhances the generalisation capabilities of deep neural networks for few-shot molecular property prediction and optimisation tasks. Next, we analyse the …
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Structure-preserving machine learning for inverse problems
… it generally requires the parts that make up the optimisation problem to be carefully chosen, and the optimisation problem may require considerable computational effort to solve. There is an active line of research into overcoming these issues using data-driven approaches, which aim to use …