Stochastic Three-Composite Convex Minimization
Authors: Alp Yurtsever, Bang Cong Vu, Volkan Cevher
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical evidence supports the effectiveness of our method in real-world problems. We present numerical evidence to assess the theoretical convergence guarantees of the proposed algorithm. We provide two numerical examples from Markowitz portfolio optimization and support vector machines. |
| Researcher Affiliation | Academia | Laboratory for Information and Inference Systems (LIONS) École Polytechnique Fédérale de Lausanne, Switzerland alp.yurtsever@epfl.ch, bang.vu@epfl.ch, volkan.cevher@epfl.ch |
| Pseudocode | Yes | Algorithm 1 Stochastic three-composite minimization algorithm (S3CM) |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use 5 different real portfolio datasets: Dow Jones industrial average (DJIA, with 30 stocks for 507 days), New York stock exchange (NYSE, with 36 stocks for 5651 days), Standard & Poor s 500 (SP500, with 25 stocks for 1276 days), Toronto stock exchange (TSE, with 88 stocks for 1258 days) that are also considered in [4]; and one dataset by Fama and French (FF100, 100 portfolios formed on size and book-to-market, 23,647 days) that is commonly used in financial literature, e.g., [6, 14]. We use UCI machine learning dataset a1a , with d = 1605 datapoints and p = 123 features [8, 18]. |
| Dataset Splits | No | The paper states "We split all the datasets into test (10%) and train (90%) partitions randomly." but does not explicitly mention a validation dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or processor types) 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 | For the deterministic algorithm, we set η = 0.1. ... We simply use γn = γ0/(n + 1) with γ0 = 1 for FF100, and γ0 = 103 for others. ... We start both algorithms from the zero vector. ... We set the desired return as the average return over all assets in the training set, b = mean(aav). and we fix problem parameters C = 1 and σ = 2 2, and we focus on the effects of algorithmic parameters on the convergence behavior. ... We use S3CM with the learning rate γn = γ0/(n + 1) for various values of γ0. |