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[null,null,"Titolo",null,"Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023 / / ed. by Martin Weiser, Aswin Kannan, Sebastian Pokutta, Kartikey Sharma, Daniel Walter, Andrea Walther, Konstantin Fackeldey"]
[null,null,"Pubbl/distr/stampa",null,"Berlin ; ; Boston : , : De Gruyter, , [2025]"]
[{"$nulls":4},"2025"]
[null,null,"ISBN",null,"3-11-137774-1"]
[{"$nulls":4},"3-11-137677-X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (X, 202 p.)"]
[null,null,"Collana",null,"De Gruyter Proceedings in Mathematics , , 2942-4801"]
[null,null,"Soggetti",null,"MATHEMATICS / Optimization"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Frontmatter -- Preface -- Acknowledgment -- Contents -- A framework to solve inverse problems for parametric PDEs using adaptive finite elements and neural networks -- Generation of value function data for bilevel optimal control and application to hybrid electric vehicle -- Graph neural networks to predict strokes from blood flow simulations -- Capturing the macroscopic behavior of molecular dynamics with membership functions -- Adaptive gradient-enhanced Gaussian process surrogates for inverse problems -- Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems -- Constrained piecewise linear optimization by an active signature method -- Parallel trust-region approaches in neural network training -- Trustworthy optimization learning: a brief overview -- Compression-aware training of neural networks using Frank–Wolfe -- Approximation of generalized frequency response functions via vector fitting -- On the nonsmooth regularity condition LIKQ for different abs-normal representations -- Divergence of the ADAM algorithm with fixed-stepsize: a (very) simple example -- Index"]
[null,null,null,"Sommario/riassunto","Mathematical optimization and machine learning are closely related. This proceedings volume of the Thematic Einstein Semester 2023 of the Berlin Mathematics Research Center MATH+ collects recent progress on their interplay in topics such as discrete optimization, "]
[{"$nulls":4},"nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning."]