Stochastic Extragradient with Flip-Flop Shuffling & Anchoring: Provable Improvements
Authors: Jiseok Chae, Chulhee Yun, Donghwan Kim
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We consider randomly generated quadratic problems... We ran the experiment on 5 random instances... results are plotted in Figure 1. ... Numerical computations are done using Num Py [24] and Sci Py [52], and the plots are drawn using Matplotlib [26]. |
| Researcher Affiliation | Academia | Jiseok Chae Department of Mathematical Sciences KAIST Daejeon, Republic of Korea jsch@kaist.ac.kr Chulhee Yun Kim Jaechul Graduate School of AI KAIST Seoul, Republic of Korea chulhee.yun@kaist.ac.kr Donghwan Kim Department of Mathematical Sciences KAIST Daejeon, Republic of Korea donghwankim@kaist.ac.kr |
| Pseudocode | Yes | We present the pseudocode of the algorithms we consider in this paper in Algorithms 2, 3 and 4, with the pseudocode of the with-replacement stochastic methods in Algorithm 1. (Appendix A) |
| Open Source Code | No | The paper states 'We have also submitted the exact code that we used for our experiments as a supplemental material' in the Neur IPS Paper Checklist section, which is outside the main paper content. The main paper body does not contain an explicit statement or link to open-source code. |
| Open Datasets | No | We consider randomly generated quadratic problems of the form min x Rdx max y Rdy 1/n Pn i=1 fi(x, y). ... For an experiment for the monotone case, the random components are sampled as follows. We choose Bi so that each element is an i.i.d. sample from a uniform distribution over the interval [0, 1]... We repeat the exact same procedure for Ci as well. |
| Dataset Splits | No | We ran the experiment on 5 random instances of (13) with the stepsizes scheduled as ηk = η0/(1+k/10)0.34 where η0 = min{0.01, 1/L} for SEG-FFA, and αk = βk = ηk for SEG-US, SEG-RR, and SEG-FF. |
| Hardware Specification | No | The paper mentions 'Numerical computations are done using Num Py [24] and Sci Py [52], and the plots are drawn using Matplotlib [26]' but does not provide specific details about the hardware (e.g., CPU/GPU models, memory) used for the experiments. |
| Software Dependencies | No | Numerical computations are done using Num Py [24] and Sci Py [52], and the plots are drawn using Matplotlib [26]. |
| Experiment Setup | Yes | We choose dx = dy = 20 and n = 40 for all the experiments. ... The stepsizes scheduled as ηk = η0/(1+k/10)0.34 where η0 = min{0.01, 1/L} for SEG-FFA, and αk = βk = ηk for SEG-US, SEG-RR, and SEG-FF. |