A unifying theory of exactness of linear penalty functions II: parametric penalty functions [PDF]
Optimization, 2017 In this article we develop a general theory of exact parametric penalty functions for constrained optimization problems. The main advantage of the method of parametric penalty functions is the fact that a parametric penalty function can be both smooth and exact unlike the standard (i.e. non-parametric) exact penalty functions that are always nonsmooth.
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Exact penalty functions with multidimensional penalty parameter and adaptive penalty updates [PDF]
Optimization Letters, 2021 In the second version, a number of small mistakes found in the paper was ...
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Human-Centric Contingency Analysis Metrics for Evaluating Operator Performance and Trust
IEEE Access, 2023 A novel set of system-state and control-action penalty functions are introduced as an alternative to traditional performance index contingency ranking.
Alexander A. Anderson +6 more
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Spatially regularized estimation for the analysis of DCE-MRI data [PDF]
, 2012 Competing compartment models of different complexities have been used for the quantitative analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging data.
Gertheiss, Jan +2 more
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New Exact Penalty Functions for Nonlinear Constrained Optimization Problems
Abstract and Applied Analysis, 2014 For two kinds of nonlinear constrained optimization problems, we propose two simple penalty functions, respectively, by augmenting the dimension of the primal problem with a variable that controls the weight of the penalty terms.
Bingzhuang Liu, Wenling Zhao
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Научный вестник МГТУ ГА, 2018 The article deals with the multi-criteria task of routing and scheduling of unmanned and manned aircraft using the method of penalty functions. The authors describe the urgency of the problem being solved for the airline management under the conditions ...
G. N. Lebedev +3 more
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Variable selection using MM algorithms [PDF]
, 2005 Variable selection is fundamental to high-dimensional statistical modeling. Many variable selection techniques may be implemented by maximum penalized likelihood using various penalty functions.
Hunter, David R., Li, Runze
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Variable selection for the multicategory SVM via adaptive sup-norm regularization [PDF]
, 2008 The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatically and therefore its solution typically utilizes ...
Liu, Yufeng +3 more
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Death penalty as a special type of punishment in the context of human rights
Journal of Modern Science, 2021 Objectives Compilation and comparison of the period when the death penalty was applied, its functions and significance, to the present day, when the death penalty has been abolished.
Magdalena Anna Rawa
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Structured penalties for functional linear models---partially empirical eigenvectors for regression [PDF]
, 2011 One of the challenges with functional data is incorporating spatial structure, or local correlation, into the analysis. This structure is inherent in the output from an increasing number of biomedical technologies, and a functional linear model is often ...
Feng, Ziding +2 more
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