Accelerated Stochastic Optimization Methods under Quasar-convexity
Authors: Qiang Fu, Dongchu Xu, Ashia Camage Wilson
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate our methods on example (6) in the Euclidean setting using synthetic dataset... The simulation results in Figure 1 validate our methods and show the superiority of our methods in terms of the convergence speed and the overall complexity. |
| Researcher Affiliation | Academia | 1 Sun Yat-sen University, Guangzhou, China 2 Harvard University, Cambridge, MA, USA 3 MIT, Cambridge, MA, USA. |
| Pseudocode | Yes | Algorithm 1 (Ak, Bk, y0, t, ϵ)... Algorithm 2 (Ak, Bk, bk, y0, p, q, ϵ)... Algorithm 3 Bisearch(f, y, z, b, c, ϵ,[guess]) |
| Open Source Code | Yes | Code is available at https://github.com/Qiang Fu09/Stochastic-quasar-convexacceleration. |
| Open Datasets | Yes | We generate the true dynamical system and data the same way as in Hardt et al. (2016) using parameters N = 5000, d = 20, T = 500. We use the following multi-classification dataset from Dua & Graff (2017), which we treat as binary classification datasets. |
| Dataset Splits | No | The paper mentions generating N training examples and using synthetic datasets or cited datasets, but it does not specify any training/validation/test dataset splits, percentages, or methodologies for partitioning data into these sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers, such as programming languages, libraries, or frameworks used for implementation. |
| Experiment Setup | Yes | We choose ϵ = 10^-2, the stepsize to be 5 * 10^-5, 1 * 10^-6, 1 * 10^-4 for SGD, L = 1 * 10^6, 1 * 10^8, 1 * 10^5 for QASGD and L = 3 * 10^4, 1 * 10^6, 1 * 10^4 for QASVRG in LDS1, LDS2 and LDS3. The flat line in the third column means the loss blows up to infinity with this choice of stepsize. ... We choose the value of random seed to be in {0, 12, 24, 36, 48}... |