Beskrivning
The research introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM) to address large-scale regularized Sparse Machine Learning (SML) problems over a network of computational nodes. These problems involve minimizing local convex loss functions over a global decision vector with an explicit $\ell_0$ norm constraint to ensure sparsity. This approach generalizes various sparse regression and classification models, including sparse linear and logistic regression, sparse softmax regression, and sparse support vector machines. Bi-cADMM reformulates the original non-convex SML problem using bi-linear consensus and employs a hierarchical decomposition strategy. This strategy splits the problem into smaller, parallel-computable sub-problems through a two-phase approach: initial sample decomposition and distribution of local datasets across nodes, followed by a delayed feature decomposition on GPUs available to each node. GPUs handle data-intensive computations, while CPUs manage less demanding tasks. The algorithm is implemented in an open-source Python package called Parallel Sparse Fitting Toolbox (PsFiT). Computational experiments validate the efficiency and scalability of Bi-cADMM through numerical benchmarks on various SML problems with distributed datasets.| Period | 30 juni 2024 → 4 juli 2024 |
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| Evenemangstitel | 33rd European Conference on Operational Research |
| Typ av evenemang | Konferens |
| Plats | Copenhagen, DanmarkVisa på karta |
| Omfattning | Internationell |