Search Results - "Difference of convex functions algorithm"
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Unknown-but-bounded uncertainty propagation in spacecraft structural system: Interval reduced basis method and its integrated framework
ISSN: 1270-9638, 1626-3219Published: Elsevier Masson SAS 01.09.2019Published in Aerospace science and technology (01.09.2019)“…Uncertainty propagation analysis is a key issue for the safety and optimal design of spacecraft structures. This paper proposes an interval reduced basis…”
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Personalized Dose Finding Using Outcome Weighted Learning
ISSN: 0162-1459, 1537-274X, 1537-274XPublished: United States Taylor & Francis 01.12.2016Published in Journal of the American Statistical Association (01.12.2016)“… To estimate the optimal IDR using such data, we propose an outcome weighted learning method based on a nonconvex loss function, which can be solved efficiently using a difference of convex functions algorithm…”
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Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve
ISSN: 0277-6715, 1097-0258Published: England Blackwell Publishing Ltd 20.09.2016Published in Statistics in medicine (20.09.2016)“…In biomedical studies, it is often of interest to classify/predict a subject's disease status based on a variety of biomarker measurements…”
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PhaseLiftOff: an Accurate and Stable Phase Retrieval Method Based on Difference of Trace and Frobenius Norms
ISSN: 2331-8422Published: Ithaca Cornell University Library, arXiv.org 07.10.2014Published in arXiv.org (07.10.2014)“… The quadratic measurement becomes linear in terms of the rank-one matrix \(X = x x^*\). Phase retrieval is then a rank-one minimization problem subject to linear constraint for which a convex relaxation based on trace-norm minimization (PhaseLift…”
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Point Source Super-resolution Via Non-convex L1 Based Methods
ISSN: 0885-7474, 1573-7691Published: New York Springer US 01.09.2016Published in Journal of scientific computing (01.09.2016)“… ), subject to the measurement constraints. In one and two dimensional numerical SR examples, the local optimal solutions from difference of convex function algorithms…”
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