Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization
Authors: Deeksha Adil, Brian Bullins, Sushant Sachdeva
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide several algorithms for constrained optimization of a large class of convex problems... As our main contribution, we initiate a new direction of study by presenting the first unified approach to achieving m1/3-type rates. Notably, our method goes beyond these previously considered problems to more broadly capture quasi-selfconcordant losses... We first present in Section 3 a width-reduced method for obtaining a crude approximation to (1) for quasi-self-concordant f . At a high level, our algorithm returns an approximate solution x that both satisfies the linear constraints and is bounded in 1-norm by O(R), where R is a bound on the norm of the optimal solution. Our algorithm is based on combining a multiplicative weight update (MWU) scheme with width reduction. The paper focuses on theoretical algorithm design, mathematical proofs, and convergence rate analysis without empirical evaluation. |
| Researcher Affiliation | Collaboration | Deeksha Adil University of Toronto EMAIL Brian Bullins TTI Chicago bbullins.ttic.edu Sushant Sachdeva University of Toronto EMAIL |
| Pseudocode | Yes | Algorithm 1 Width-Reduced Algorithm for M-q.s.c. Functions, Algorithm 2 Boosting to -approximation, Algorithm 3 |
| Open Source Code | No | The paper does not provide any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not describe or use any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not include any information about dataset splits (training, validation, or test). |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used for computations. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |