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Corpus ID: 229153938

On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation

@article{Bieker2020OnTT,
title={On the Treatment of Optimization Problems with L1 Penalty Terms via Multiobjective Continuation},
author={K. Bieker and Bennet Gebken and Sebastian Peitz},
journal={ArXiv},
year={2020},
volume={abs/2012.07483}
}

We present a novel algorithm that allows us to gain detailed insight into the effects of sparsity in linear and nonlinear optimization, which is of great importance in many scientific areas such as image and signal processing, medical imaging, compressed sensing, and machine learning (e.g., for the training of neural networks). Sparsity is an important feature to ensure robustness against noisy data, but also to find models that are interpretable and easy to analyze due to the small number of… Expand