Mathematical Optimization for Machine Learning
eBook - ePub

Mathematical Optimization for Machine Learning

Proceedings of the MATH+ Thematic Einstein Semester 2023

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Mathematical Optimization for Machine Learning

Proceedings of the MATH+ Thematic Einstein Semester 2023

About this book

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, nonlinear programming, optimal control, first-order methods, multilevel optimization, machine learning in optimization, physics-informed learning, and fairness in machine learning.

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Yes, you can access Mathematical Optimization for Machine Learning by Konstantin Fackeldey,Aswin Kannan,Sebastian Pokutta,Kartikey Sharma,Daniel Walter,Andrea Walther,Martin Weiser in PDF and/or ePUB format, as well as other popular books in Mathematics & Artificial Intelligence (AI) & Semantics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Title Page
  2. Copyright
  3. Contents
  4. A framework to solve inverse problems for parametric PDEs using adaptive finite elements and neural networks
  5. Generation of value function data for bilevel optimal control and application to hybrid electric vehicle
  6. Graph neural networks to predict strokes from blood flow simulations
  7. Capturing the macroscopic behavior of molecular dynamics with membership functions
  8. Adaptive gradient-enhanced Gaussian process surrogates for inverse problems
  9. Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems
  10. Constrained piecewise linear optimization by an active signature method
  11. Parallel trust-region approaches in neural network training
  12. Trustworthy optimization learning: a brief overview
  13. Compression-aware training of neural networks using Frank–Wolfe
  14. Approximation of generalized frequency response functions via vector fitting
  15. On the nonsmooth regularity condition LIKQ for different abs-normal representations
  16. Divergence of the ADAM algorithm with fixed-stepsize: a (very) simple example
  17. Subject Index