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Smoothed Moreau|Yosida Tensor Train Approximation of State ...
Smoothed Moreau-Yosida Tensor Train Approximation of State ...
We propose an algorithm to solve optimization problems constrained by partial (ordinary) differential equations under uncertainty, with almost sure constraints ...
State-Constrained Optimization Problems Under Uncertainty
To enable efficient tensor-train approximation of the state ... fast matrix-free action of the approximate Hessian to solve the smoothed Moreau-Yosida.
Tensor approximation for almost sure constraints - arXiv
SMOOTHED MOREAU-YOSIDA TENSOR TRAIN APPROXIMATION OF. STATE-CONSTRAINED OPTIMIZATION PROBLEMS UNDER UNCERTAINTY∗. HARBIR ANTIL† , SERGEY ...
Smoothed Moreau-Yosida Tensor Train Approximation of State ...
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Smoothed Moreau-Yosida Tensor Train Approximation of State ...
Article "Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty" Detailed information of the ...
State-constrained Optimization Problems under Uncertainty
To enable efficient tensor-train approximation of the state ... approximate Hessian to solve the smoothed Moreau-Yosida problem. This ...
[PDF] Constrained Optimization with Low-Rank Tensors and ...
Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty · Physics, Computer Science · 2023.
Onwunta, Smoothed Moreau-Yosida tensor train approximation of state-constrained optimization problems under uncertainty, 2023. H. Antil, H. C. Elman, A ...
Tensor train decomposition algorithm for risk averse optimization
Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty · Physics, Computer Science · 2023.
State-constrained Optimization Problems under Uncertainty ... - X-MOL
... tensor-train decomposition. To enable efficient tensor-train approximation of the state ... approximate Hessian to solve the smoothed Moreau-Yosida problem.
Rank Bounds for Approximating Gaussian Densities in the Tensor ...
To enable efficient tensor-train approximation of the state constraints, the ... approximate Hessian to solve the smoothed Moreau-Yosida problem. This ...
Articles by Harbir Antil - Synthical
Smoothed Moreau-Yosida Tensor Train Approximation of State-constrained Optimization Problems under Uncertainty. 4 July 2024 by Harbir Antil and others.
A Simple and Efficient Smoothing Method for Faster Optimization ...
domized smoothing (RS) and the approximation efficiency of the Moreau envelope ... state of the art architectures remains difficult due to the highly non-smooth.
Solving differential Riccati equations: A nonlinear space-time ...
... tensor train approximation P ~ (cf. Section 4). Figure 3. Storage reduction ... Wathen, Preconditioners for state-constrained optimal control problems with Moreau ...
Two heuristics solving low tensor train rank tensor completion
Many state-of-the-art algorithms are now available for model (3), e.g., the SVT algorithm [5] and the FPCA algorithm [30]. Besides the nuclear norm heuristic, ...
Nonlinear Optimization and Inverse Problems - Publications
Schillings, Generative modelling with tensor train approximations of Hamilton--Jacobi--Bellman equations, Preprint no. 3078, WIAS, Berlin, 2023, DOI ...
... Train Sketching. Ziang ... Additionally, we establish an efficiency-guaranteed approximation of the quantum ground states using Matrix Product States (MPS).
Summer School - Numerical Methods for Random Differential Models
For state-constrained problems, Moreau-Yosida regularization can be ... The adaptive approach balances the smoothing and tensor approximation errors.
Enhancing Value Function Estimation through First-Order State-Action Dynamics in Offline Reinforcement Learning ... Coarse-To-Fine Tensor Trains for Compact ...
... tensor-train (TT) format. In the online phase, the resulting ... In this talk, we will discuss smoothing functions resembling the Moreau envelope, ...