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..
The inexact power augmented Lagrangian method for constrained nonconvex optimization
Authors: Alexander Bodard, Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, numerical experiments validate the practical performance of unconventional augmenting terms. |
| Researcher Affiliation | Collaboration | Alexander Bodard EMAIL ESAT-STADIUS & Leuven.AI, KU Leuven Konstantinos Oikonomidis EMAIL ESAT-STADIUS & Leuven.AI, KU Leuven Emanuel Laude EMAIL Proxima Fusion Gmb H Panagiotis Patrinos EMAIL ESAT-STADIUS & Leuven.AI, KU Leuven |
| Pseudocode | Yes | Algorithm 1 Inexact power augmented Lagrangian method Algorithm 2 Inexact proximal point method for (13) |
| Open Source Code | Yes | All experiments are run in Julia on an HP Elite Book with 16 cores and 32 GB memory, and the source code is publicly available.1 1https://github.com/alexanderbodard/tmlr_nonconvex_power_alm |
| Open Datasets | Yes | We test Algorithm 1 on two problem instances, being the MNIST dataset Deng (2012) and the Fashion MNIST dataset Xiao et al. (2017). |
| Dataset Splits | Yes | The setup is similar to that of Sahin et al. (2019), which is in turn based on Mixon et al. (2016). In particular, a simple two-layer neural network was used to first extract features from the data, and then this neural network was applied to n = 1000 random test samples from the dataset, yielding the vectors {zi}n=1000 i=1 that generate the distance matrix D. |
| Hardware Specification | Yes | All experiments are run in Julia on an HP Elite Book with 16 cores and 32 GB memory |
| Software Dependencies | No | All experiments are run in Julia on an HP Elite Book with 16 cores and 32 GB memory, and the source code is publicly available.1 |
| Experiment Setup | Yes | We define s = 10, r = 20, tune σ1 = 10, λ = 10 3, β1 = 5, ω = 1.1, and impose a maximum of N = 1500 UPFAG iterations per subproblem. ... We use tolerances εφ = εA = 10 3. |