Adaptive Algorithms for Online Convex Optimization with Long-term Constraints
Authors: Rodolphe Jenatton, Jim Huang, Cedric Archambeau
ICML 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We supplement the analysis with experiments validating the performance of our algorithm in practice.We ran two sets of experiments to assess the performance for our adaptive algorithms for OCO with long-term constraints and compare to the algorithms proposed by Mahdavi et al. (2012a). |
| Researcher Affiliation | Industry | Rodolphe Jenatton , Jim C. Huang , Cedric Archambeau {JENATTON,HUANGJIM,CEDRICA}@AMAZON.COM Amazon, Berlin, Germany, Seattle, USA |
| Pseudocode | Yes | Initialize x1 = 0 and λ1 = 0. For t {1, , T 1}: xt+1 = ΠB(xt ηt x Lt(xt, λt)), λt+1 = ΠR+(λt + µt λLt(xt, λt)), |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We solve the above problem using the datasets ijcnn1 and covtype, consisting respectively of 49, 990 and 581, 012 samples of dimension d = 22 and d = 54 each.2 www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/binary.html. |
| Dataset Splits | No | The paper describes using datasets 'ijcnn1' and 'covtype' and discusses generating sequences, but it does not specify explicit train/validation/test splits, percentages, or sample counts for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using 'CVXPY' and an implementation based on 'Defazio et al. (2014)' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | The parameter ρ is set to obtain approximately 30% of nonzero variables. They are computed over T = 1000 iterations with d = 64, and are averaged over 10 random sequences {Yt}T t=1. |