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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
| 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 |
| Autore | Weiser Martin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berlin/Boston, : De Gruyter, 2025 |
| Descrizione fisica | 1 online resource (X, 202 p.) |
| Collana | De Gruyter Proceedings in Mathematics |
| Soggetto topico | MATHEMATICS / Optimization |
| Soggetto non controllato | Mathematical optimization, Machine learning, Nonlinear optimization, Discrete optimization, Physics informed learning |
| ISBN |
3-11-137774-1
3-11-137677-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 |
| Record Nr. | UNINA-9911024074903321 |
Weiser Martin
|
||
| Berlin/Boston, : De Gruyter, 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
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
| 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 |
| Autore | Weiser Martin |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berlin/Boston, : De Gruyter, 2025 |
| Descrizione fisica | 1 online resource (X, 202 p.) |
| Collana | De Gruyter Proceedings in Mathematics |
| Soggetto topico | MATHEMATICS / Optimization |
| Soggetto non controllato | Mathematical optimization, Machine learning, Nonlinear optimization, Discrete optimization, Physics informed learning |
| ISBN |
3-11-137774-1
3-11-137677-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 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 |
| Record Nr. | UNISA-996657772003316 |
|
Weiser Martin
|
||
| Berlin/Boston, : De Gruyter, 2025 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||