SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization

Songtao Lu
2/12/2026

No confident code implementation found for this paper. 1 possible match can be shown below.

DOISemantic Scholar

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. 1 weaker match is hidden by default — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Norma: A Noise Robust Memory-Augmented Framework for Whole Slide Image Classification

100% match2026

S2MAE: A Spatial-Spectral Pretraining Foundation Model for Spectral Remote Sensing Data

100% match2026

Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting

100% match1 repo7032026

CPCF: A Cross-Prompt Contrastive Framework for Referring Multimodal Large Language Models

100% match2026

SynMob: Creating High-Fidelity Synthetic GPS Trajectory Dataset for Urban Mobility Analysis

100% match2026

Quamba: A Post-Training Quantization Recipe for Selective State Space Models

100% match2026

Cite this paper

@article{lu2026slm,
  title  = {SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization},
  author = {Songtao Lu},
  year   = {2026},
  doi    = {10.52202/075280-3526},
  url    = {https://doi.org/10.52202/075280-3526},
  journal = {NEURIPS 2023 2023}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/245e67d4acd761b1d5b82bd5279803a48831c1bf.svg)](https://codeofpaper.com/paper/245e67d4acd761b1d5b82bd5279803a48831c1bf)

Discussion