Deep Relaxation of Controlled Stochastic Gradient Descent via Singular Perturbations-Reference-Cited by-同舟云学术

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Deep Relaxation of Controlled Stochastic Gradient Descent via Singular Perturbations

Published:2024-07-24 Issue:4 Volume:62 Page:2229-2253
ISSN:0363-0129
Container-title:SIAM Journal on Control and Optimization
language:en
Short-container-title:SIAM J. Control Optim.

Author:

Bardi Martino¹^ORCID,Kouhkouh Hicham²^ORCID

Affiliation:

1. Department of Mathematics “T. Levi-Civita”, University of Padova, I-35121 Padova, Italy.

2. RWTH Aachen University, Institut für Mathematik, RTG Energy, Entropy, and Dissipative Dynamics, Templergraben 55 (111810), 52062, Aachen, Germany, Current address: Department of Mathematics and Scientific Computing, NAWI, University of Graz, 8010, Graz, Austria.

Funder

Deutsche Forschungsgemeinschaft

Publisher

Society for Industrial & Applied Mathematics (SIAM)

Reference30 articles.

1. Subdominant Dense Clusters Allow for Simple Learning and High Computational Performance in Neural Networks with Discrete Synapses

2. Optimal Control with Random Parameters: A Multiscale Approach

3. Convergence by Viscosity Methods in Multiscale Financial Models with Stochastic Volatility

4. Singular perturbations in stochastic optimal control with unbounded data

5. Matrix Analysis

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