Delay-agnostic Asynchronous Coordinate Update Algorithm
Authors: Xuyang Wu, Changxin Liu, Sindri Magnússon, Mikael Johansson
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of DEGAS is demonstrated by numerical experiments on classification problems. We evaluate the practical performance of DEGAS on Lasso and regularized logistic regression problems on the CIFAR-100 dataset (Krizhevsky et al., 2009). We plot the convergence in terms of the number of computed Ti in Figures 2 3 |
| Researcher Affiliation | Academia | 1Division of Decision and Control Systems, EECS, KTH Royal Institute of Technology, Stockholm, Sweden 2Department of Computer and System Science, Stockholm University, Stockholm, Sweden. |
| Pseudocode | Yes | Algorithm 1 DEGAS 1: Setup: initial iterate x(0). 2: Initialization: the master sets x = x(0) and broadcasts x to all workers. 3: while not interrupted by master: each worker w [n] asynchronously and continuously do 4: receive x from the master and assign xw = x. 5: sample i [m] uniformly at random. 6: compute Ti(xw). 7: send (Ti(xw), i) to the master. 8: end while 9: while not converged: the master do 10: receive (Ti(xw), i) from a worker w. 11: update xi Ti(xw). 12: send x to the worker w. 13: end while |
| Open Source Code | No | The paper does not provide a direct link to a source code repository or an explicit statement of code release for the methodology described. |
| Open Datasets | Yes | We evaluate the practical performance of DEGAS on Lasso and regularized logistic regression problems on the CIFAR-100 dataset (Krizhevsky et al., 2009). |
| Dataset Splits | No | The paper mentions using the CIFAR-100 dataset but does not explicitly provide details about specific train/validation/test splits, such as percentages or sample counts. |
| Hardware Specification | Yes | We set m = 20 and implement all the methods on a 10-core machine (1 master and 9 workers) |
| Software Dependencies | No | The paper mentions 'MPI4py (Dalcın et al., 2008)' but does not provide specific version numbers for MPI4py or any other software dependencies, which are required for reproducibility. |
| Experiment Setup | Yes | In these methods, we choose the operator T as (20) with γ = 1/L in BCD and (29) in the extended ADMM. We set m = 20... We consider both theoretical and hand-tuned parameters. In the former setting, we fine-tune the step-size of ARock within its theoretical range... while the other two methods have no parameters to tune. In the hand-tune step-size setting, we run all the methods for finding the fixed point of Id +λ(T Id), λ > 0 and tune λ. |