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..
Optimization Algorithm Design via Electric Circuits
Authors: Stephen Boyd, Tetiana Parshakova, Ernest Ryu, Jaewook J. Suh
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we use our methodology to obtain a new algorithm and experiment with it on a specific problem instance. ... We apply DADMM+C to the decentralized optimization problem and observe a speedup as shown in the right columns of Figure 9. The relative error for DADMM+C decreases to 10 10 in 66 iterations, for DADMM in 87 iterations and for P-EXTRA in 294 iterations. |
| Researcher Affiliation | Academia | Stephen P. Boyd Stanford University Tetiana Parshakova Stanford University Ernest K. Ryu UCLA Jaewook J. Suh Rice University |
| Pseudocode | No | The paper presents algorithms and equations in text and mathematical notation, but it does not include explicitly labeled pseudocode or algorithm blocks/figures. |
| Open Source Code | Yes | We provide an open-source package that implements automatic discretization of our circuits: https://github.com/cvxgrp/optimization_via_circuits |
| Open Datasets | No | We set m = 100, N = 6, S = {4, 5}, and sample vectors bi R100 from the uniform distribution over [ 100, 100]100. ... The paper does not provide access to a public dataset; it describes the generation of synthetic data for its experiments. |
| Dataset Splits | No | The paper does not explicitly mention train, validation, or test dataset splits or percentages. It uses synthetic data for its experiments. |
| Hardware Specification | No | The NeurIPS Paper Checklist states 'We use minimal CPU computation for toy experiments.' (Point 8). This is a general statement and does not specify any particular CPU models, memory, or other hardware details. |
| Software Dependencies | No | The paper mentions specific software tools like 'Ipopt [155, 9] solver' and 'PEPit [76] package', and refers to a custom package 'ciropt'. However, it does not provide specific version numbers for these software dependencies, which are necessary for full reproducibility. |
| Experiment Setup | Yes | Example. Consider the following example circuit for the minimization of a convex function f. Let R1 = R2 = R3 = 1, and C1 = C2 = 10. With our automatic discretization methodology, we find the sufficiently dissipative parameters η = 6.66, h = 6.66, α = 0, β = 1. ... We consider the circuit with R = 0.8, L = 2 and C = 15. ... The sufficiently dissipative parameters we find are η = 3.70, h = 3.52, ρ = 0, α = 0, β = 1, γ = 4.48. |