· 2013
Revisiting Frank-Wolfe: Projection-Free Sparse Convex Optimization
- 898citations
- 2013year
Short summary
A new variant of the Frank-Wolfe algorithm achieves sparse solutions in convex optimization problems without requiring computationally expensive projections.
AI-generated from the title and abstract; the full text is not read.
Key points
- Introduces a projection-free variant of the Frank-Wolfe algorithm.
- Enables the efficient computation of sparse solutions for convex optimization.
- Avoids computationally expensive projection steps inherent in some optimization methods.
- Applicable to problems where sparsity is a desired outcome.
AI-generated from the title and abstract; the full text is not read.
Abstract
The authors compare recovery and the post-anaesthetic period in 45 patients anaesthetised with a combination of either pentothal, nitrous oxide, fluothane and dolosal (group 1, 24 subjects), or of pentothal, nitrous oxide and phenoperidine (group 2, 21 subjects). The quality of recovery was judged at the end of anaesthesia by determining of occurrence of the first response to 3 types of stimulation (calling the name, noise and pain) and the density of these responses during each minute. During the post-anaesthetic period the respective percentages of wakefulness and sleep were calculated. In this way the authors demonstrate a significant (P less than 0.01) shortening in recovery time for group 2, reactivity to own name occurring first in both groups, and a very significant (P less than 0.001) increase in the percentage of physiological sleep in subjects of group 2 during the post-anaesthetic stage.
The authors' abstract, as published at the source., 2013 · DOI ↗
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Field: Computational Mechanics
Computational MechanicsEngineering