Time--Data Tradeoffs by Aggressive Smoothing
Authors: John J Bruer, Joel A Tropp, Volkan Cevher, Stephen Becker
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 3 shows the results of a numerical experiment that compares the performance difference between current numerical practice and our aggressive smoothing approach. |
| Researcher Affiliation | Academia | John J. Bruer1,* Joel A. Tropp1 Volkan Cevher2 Stephen R. Becker3 1Dept. of Computing + Mathematical Sciences, California Institute of Technology 2Laboratory for Information and Inference Systems, EPFL 3Dept. of Applied Mathematics, University of Colorado at Boulder *jbruer@cms.caltech.edu |
| Pseudocode | Yes | Algorithm 3.1 Auslender Teboulle applied to the dual-smoothed RLIP |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | In the experiment, we fix both the ambient dimension d = 40 000 and the normalized sparsity ρ = 5%. To test each smoothing approach, we generate and solve 10 random sparse vector recovery models for each value of the sample size m = 12 000,14 000,16 000,...,38 000. Each random model comprises a Gaussian measurement matrix A and a random sparse vector x whose nonzero entires are 1 with equal probability. |
| Dataset Splits | No | The paper describes generating random models for various sample sizes, but does not provide specific training/test/validation dataset splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In the experiment, we fix both the ambient dimension d = 40 000 and the normalized sparsity ρ = 5%. We stop Algorithm 3.1 when the relative error x xk / x is less than 10 3. For the constant smoothing case, we choose µ = 0.1 based on the recommendation in [15]. We set the smoothing parameter µ = µ(m)/4. |