Decomposition Algorithm Model for Singly Linearly-Constrained Problems Subject to Lower and Upper Bounds

Many real applications can be formulated as nonlinear minimization problems with a single linear equality constraint and box constraints. We are interested in solving problems where the number of variables is so huge that basic operations, such as the evaluation of the objective function or the upda...

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Veröffentlicht in:	Journal of optimization theory and applications Jg. 141; H. 1; S. 107 - 126
Hauptverfasser:	Lin, C. J., Lucidi, S., Palagi, L., Risi, A., Sciandrone, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Boston Springer US 01.04.2009 Springer Springer Nature B.V
Schlagworte:	Algorithms Applications of Mathematics Applied sciences Calculus of Variations and Optimal Control; Optimization Decomposition Engineering Exact sciences and technology Mathematical programming Mathematics Mathematics and Statistics Methods Operational research and scientific management Operational research. Management science Operations Research/Decision Theory Optimization Studies Support vector machines Theory of Computation Large scale optimization Support vector machines Decomposition methods Freedom degree Program optimization Decomposition method Modeling Convex programming Vector support machine Numerical convergence Large scale system Lower bound Objective analysis Minimization Quadratic programming Global solution Exact solution Upper bound Non differentiable programming Equality constraint Sparse set Descent method Problem solving Large scale Objective function
ISSN:	0022-3239, 1573-2878
Online-Zugang:	Volltext
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