Suchergebnisse
16. Juni 2024 · Das Dreamteam SVD II, das zweimal in Folge den Titel und Aufstieg schaffte, diesmal die Meisterschaft der Landesliga. Weiterlesen: Herren II rocken nach dem Aufstieg auch die Landesliga und sichern sich den Titel
- SV Deuchelried Tischtennis – Startseite - svd-tt.de
SV Deuchelried Tischtennis – Startseite. Damen I starten mit...
- SV Deuchelried Tischtennis – Startseite - svd-tt.de
29. Jan. 2021 · We propose a `tensor-train singular value decomposition' (TT-SVD) algorithm based on two building blocks: a `Q-less tall-skinny QR' factorization, and a fused tall-skinny matrix-matrix multiplication and reshape operation.
- Melven Röhrig-Zöllner, Jonas Thies, Achim Basermann
- 2021
- Randomized SVD For Matrices
- Listing 1 Randomized Range Approximation
- Theorem 1
- Randomized Tt-Svd
- Listing 2 Randomized Tt-Svd
- Theorem 2
- Proof
- Proposition 1
Randomized techniques for the calculation of SVD or QR factorizations of matrices have been proposed many times in the literature. However it was only recently that, thanks to the application of new results from random matrix theory, these procedures could be analyzed rigorously. We start this section by presenting some results from the work of Hal...
Input : ∖(∖ mathbf A,∖ r ,∖ p∖) Output : ∖(∖ mathbf Q∖) Create a standard Gaussian random matrix ∖(∖ mathbf{ G } ∖ in ∖mathbb{ R}ˆ{n_2 ∖times ( r+ p)}∖) Calculate the intermediate matrix ∖(∖ mathbf B := ∖mathbf{ A } ∖mathbf{ G } ∖ in ∖mathbb{ R}ˆ{n_1 ∖times s }∖). Compute the factorization ∖(∖ mathbf Q ∖mathbf R = ∖mathbf B∖). The following theorem...
Given \(\mathbf A \in \mathbb {R}^{m \times n}\) and s =r +p with p ≥ 2. For the projection Q obtained by procedure 1, there holds the following error bounds. with probability at least 1 − 6p−p and for p ≥ 4 and any u, t ≥ 1 with probability at least \(1 - 5t^{-p}-2e^{-u^2/2}\). Let us highlight furthermore that for the operator norm, we have that ...
In this section we show how the same idea of the randomized range approximation for matrices can be used to formulate a randomized algorithm that calculates an approximate TT-SVD of arbitrary tensors. We show that stochastic error bounds analogous to the matrix case can be obtained. Furthermore we show that for sparse tensors, this randomized TT-SV...
Input : ∖(∖ mathbf x∖) , Output : ∖(∖ mathbf W_1, ∖ ldots , ∖mathbf W_d∖) Set ∖(∖ mathbf b_{d+1} := ∖mathbf x∖) For ∖( j= d , ∖ ldots , 2∖): Create a Gaussian random tensor ∖(∖ mathbf g ∖ in ∖mathbb{ R}ˆ{ s_{j −1} ∖ times n_1 ∖ times ∖ ldots ∖ times n_ { j −1}}∖) Calculate ∖(∖ mathbf a_{ j } := ∖mathbf g ∖ circ_ {(2 , ∖ ldots j ) , (1 , ∖ ldots , j...
Given \(\mathbf x \in \mathbb {R}^{n_1 \times \ldots \times n_d}\), s =r +p with p ≥ 4. For every u, t ≥ 1, the error of the randomized TT-SVD, as given in Listing 2, fulfills with probability at least \((1 - 5t^{-p}-2e^{-u^2/2})^{d-1}\) . The parameter η is given as
For syntactical convenience let us define \(\mathbf B_i := \left ( \hat M_{\{1, \ldots , i-1\}} ( \mathbf b_i ) \right )T\). Then as \(\hat P_{2,\ldots ,d}\)is an orthogonal projector, we have For all 2 ≤ i ≤ d, there holds where we used that \(\mathbf Q_iT \mathbf Q_i\) is an orthogonal projector as well. Inserting this iteratively into (46) gives...
Assume that \(\mathbf x \in \mathbb {R}^{n_1 \times \ldots \times n_d}\) contains at most N non-zero entries. Then the computational complexity of the randomized TT-SVD given in Listing 2 scales as \(\mathcal {O}(d(s^2 N +s^3 n))\).
- Benjamin Huber, Reinhold Schneider, Sebastian Wolf
- 2017
21. Sept. 2022 · SV Deuchelried Tischtennis – Startseite. Damen I starten mit klarem Sieg in der VOL. Geschrieben von: Walter Frick. Veröffentlicht: 02. Oktober 2022. Anne Duffner ging mit starker Leistung gegen Berg voran und führte das Team zum Sieg. Die erste Prüfung erledigte das erste Damenteam des SVD gegen den SC Berg mit Bravour.
We propose a new computationally efficient algorithm, tensor-train orthogonal iteration (TTOI), that aims to estimate the low tensor-train rank structure from the noisy high-order tensor observation. The proposed TTOI consists of initialization via TT-SVD [Oseledets (2011)] and new iterative backward/forward updates.
A naive solution is to introduce trust information. However, trust information may also face the difficulty of sparse trust evidence (also known as sparse trust problem). In our work, an accurate SDM model with two-way trust recommendation in the AI-enabled IoT systems is proposed, named TT-SVD.
Proof of Lemma can be turned into practical algorithm (TT-SVD by [Oseledets’2011]) for approximating a given tensor X in TT format: Input: X2 Rn1 nd, target TT rank (r1; : : : ; rd 1). Output: TT cores U 2 Rr. 1 n r that define a TT decomposition approximating X.
