18 citations · 29 across the 6 of their papers we have counts for
6 papers
Support Discovery With Iteratively Reweighted Least Squares for Fixed-Charge Network Flow
Sindura Saraswathi, Christian Kümmerle
The fixed-charge network flow problem (FCNFP) couples continuous flow allocation with discrete arc-activation decisions, making it a canonical but computationally challenging model…
Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS
Christian Kümmerle, Tomas Masak, Dominik Stöger
Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role of the weight operator have…
UnitNorm: Rethinking Normalization for Transformers in Time Series
Nan Huang, Christian Kümmerle, Xiang Zhang
Normalization techniques are crucial for enhancing Transformer models' performance and stability in time series analysis tasks, yet traditional methods like batch and layer normali…
Recovering Simultaneously Structured Data via Non-Convex Iteratively Reweighted Least Squares
Christian Kümmerle, Johannes Maly
We propose a new algorithm for the problem of recovering data that adheres to multiple, heterogeneous low-dimensional structures from linear observations. Focusing on data matrices…
Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate
Christian Kümmerle, Claudio Mayrink Verdun, Dominik Stöger
The recovery of sparse data is at the core of many applications in machine learning and signal processing. While such problems can be tackled using -regularization as in th…
Harmonic Mean Iteratively Reweighted Least Squares for Low-Rank Matrix Recovery
Christian Kümmerle, Juliane Sigl
We propose a new iteratively reweighted least squares (IRLS) algorithm for the recovery of a matrix of rank from incomplete…