Optimal and Adaptive Monteiro-Svaiter Acceleration
Authors: Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we report empirical results (Section 4).3 On logistic regression problems, combining our optimal acceleration scheme with our adaptive oracle outperforms previously proposed accelerated second-order methods. |
| Researcher Affiliation | Academia | Tel Aviv University, ycarmon@tauex.tau.ac.il, hausler@mail.tau.ac.il Stanford University, {jmblpati,yujiajin,sidford}@stanford.edu |
| Pseudocode | Yes | Algorithm 1: Optimal MS Acceleration |
| Open Source Code | Yes | 3The code for our experiments is available at https://github.com/danielle-hausler/ms-optimal. |
| Open Datasets | Yes | On logistic regression on the a9a dataset [15] |
| Dataset Splits | No | The paper mentions using specific datasets but does not provide explicit details on how they were split into training, validation, and test sets (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'L-BFGS-B from Sci Py' and refers to 'Scikit-learn: Machine learning in Python' in its references, but does not specify version numbers for these or other software dependencies crucial for replication. |
| Experiment Setup | Yes | For all runs, we set the initial point x0 = 0 and use a batch size of 100 for mini-batch stochastic gradient descent. We implement the adaptive methods using λ0 = 1, σ = 0.5, and = 2. |