Approximate Multiplication of Sparse Matrices with Limited Space
Authors: Yuanyu Wan, Lijun Zhang10058-10066
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we perform numerical experiments to verify the efficiency and effectiveness of our SCOD. Fig. 1 and 2 show the results of different algorithms among different ℓon the synthetic datasets. |
| Researcher Affiliation | Academia | Yuanyu Wan, Lijun Zhang National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China {wanyy, zhanglj}@lamda.nju.edu.cn |
| Pseudocode | Yes | Algorithm 1 Dense Shrinkage (DS), Algorithm 2 Simultaneous Iteration (SI), Algorithm 3 Verified Simultaneous Iteration (VSI), Algorithm 4 Sparse Co-occuring Directions (SCOD) |
| Open Source Code | No | The paper does not provide a specific repository link or an explicit statement about the release of source code for the described methodology. |
| Open Datasets | Yes | NIPS conference papers1 (Perrone et al. 2017) and Movie Lens 10M2. ... 1https://archive.ics.uci.edu/ml/datasets/NIPS+Conference+ Papers+1987-2015 2https://grouplens.org/datasets/movielens/10m/ |
| Dataset Splits | No | The paper describes how the input matrices X and Y are derived from the original datasets (e.g., 'let XT be the first 2905 columns of M, and let Y T be the others'), but it does not specify train/validation/test splits for model evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Matlab' as used for generating synthetic datasets but does not specify a version number or list other software dependencies with their versions for the implementation of the algorithm. |
| Experiment Setup | Yes | In all experiments, each algorithm will receive two matrices X Rmx n and Y Rmy n, and then output two matrices BX Rmx ℓand BY Rmy ℓ. We adopt the approximation error XY T BXBT Y and the projection error XY T π U(X)π V (Y )T to measure the accuracy of each algorithm, where U Rmx k, V Rmy k and we set k = 200. Furthermore, we report the runtime of each algorithm to verify the efficiency of our SCOD. Because of the randomness of SCOD, SFD-AMM, CS, RP and Hashing, we report the average results over 50 runs. |