Karush-Kuhn-Tucker conditions and Lagrangian approach for improving machine learning techniques: a survey and new developments
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Publication:6646827
DOI10.1478/aapp.1021a1MaRDI QIDQ6646827
Massimiliano Ferrara, Tiziana Ciano
Publication date: 3 December 2024
Published in: Atti della Accademia Peloritana dei Pericolanti. Classe di Scienze Fisiche, Matemàtiche e Naturali (Search for Journal in Brave)
Convex programming (90C25) Nonlinear programming (90C30) Learning and adaptive systems in artificial intelligence (68T05)
Cites Work
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- Enhanced Karush-Kuhn-Tucker condition and weaker constraint qualifications
- New order relations in set optimization
- On approximate KKT condition and its extension to continuous variational inequalities
- Approximate Karush-Kuhn-Tucker condition in multiobjective optimization
- Modified Kuhn-Tucker conditions when a minimum is not attained
- Discrete support vector decision trees via tabu search
- The algebra of many-valued quantities
- On generalized derivatives for \(C^{1,1}\) vector optimization problems
- Support-vector networks
- Extended Karush-Kuhn-Tucker condition for constrained interval optimization problems and its application in support vector machines
- Approximate KKT points and a proximity measure for termination
- Karush-Kuhn-Tucker conditions in set optimization
- Maximal margin classification for metric spaces
- A New Sequential Optimality Condition for Constrained Optimization and Algorithmic Consequences
- Asymptotic kuhn-tucker conditions in abstract spaces
- On cone convexity of set-valued maps
- Extensions of Asymptotic Kuhn–Tucker Conditions in Mathematical Programming
- Data Mining
- Asymptotic Lagrange Regularity for Pseudoconcave Programming with Weak Constraint Qualification
- The Slacked Unconstrained Minimization Technique for Convex Programming
- On sequential optimality conditions for smooth constrained optimization
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