The inexact power augmented Lagrangian method for constrained nonconvex optimization
Authors: Alexander Bodard, Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos
TMLR 2025 | Venue PDF | Archive PDF | Plain Text | 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. |