On a Combination of Alternating Minimization and Nesterov’s Momentum
Authors: Sergey Guminov, Pavel Dvurechensky, Nazarii Tupitsa, Alexander Gasnikov
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The practical efficiency of the algorithm is demonstrated by a series of numerical experiments. In this section we apply our general accelerated AM method to a non-convex collaborative filtering problem. [...] In Figure 1 we compare the performance of AM and Algorithm 1 applied to the problem (7). [...] In Figure 2, we provide a numerical comparison of our methods with Sinkhorn s algorithm, the AAR-BCD method [...] We performed experiments using randomly chosen images from MNIST dataset. [...] In Section 6 we provide numerical experiment for least squares problem for linear regression. |
| Researcher Affiliation | Academia | 1Moscow Institute of Physics and Technology, Dolgoprudny, Russia 2Institute for Information Transmission Problems RAS, Moscow, Russia 3HDI Lab @ National Research University Higher School of Economics, Russian Federation 4Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany. |
| Pseudocode | Yes | Algorithm 1 Accelerated Alternating Minimization (AAM) [...] Algorithm 2 Primal-Dual AAM |
| Open Source Code | Yes | Code for all presented algorithms is available at https:// github.com/nazya/AAM |
| Open Datasets | Yes | We generate the matrix {rui}u,i from Last.fm dataset 360K [...] We performed experiments using randomly chosen images from MNIST dataset. [...] We also illustrate the results by solving the alternating least squares problem on the Blog Feedback Data Set (Buza, 2014) obtained from UCI Machine Learning Repository. |
| Dataset Splits | No | The paper describes the datasets used and how they are partitioned for the alternating minimization method itself (e.g., into blocks), but does not provide explicit training, validation, and test split percentages or counts for reproducibility of data partitioning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, memory, or cloud resources used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers that would be needed to replicate the experiments (e.g., Python version, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | The regularization coefficient was set to λ = 0.1 [...] Parameter of entropic regularization γ = 5e 4. [...] Parameter of entropic regularization γ = 5e 5. |