9786136544830 - Convex Optimization: Mathematical optimization, Convex function, Convex analysis (4 risultati)

Taschenbuch. Condizione: Neu. Convex Optimization | Mathematical optimization, Convex function, Convex analysis | Eldon A. Mainyu | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786136544830 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 116 pp. Englisch.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Convexoptimization, a subfield of mathematical optimization, studies theproblem of minimizing convex functions. Convex minimization hasapplications in a wide range of disciplines, such as automatic controlsystems, estimation and signal processing, communications and networkselectronic circuit design, data analysis and modeling, statistics, andfinance. With recent improvements in computing and in optimizationtheory, convex minimization is nearly as straightforward as linearprogramming. These results are used by the theory of convex minimizationalong with geometric notions from functional analysis such as theHilbert projection theorem, the separating hyperplane theorem, andFarkas' lemma.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 116 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articlesavailable from Wikipedia or other free sources online. Convexoptimization, a subfield of mathematical optimization, studies theproblem of minimizing convex functions. Convex minimization hasapplications in a wide range of disciplines, such as automatic controlsystems, estimation and signal processing, communications and networkselectronic circuit design, data analysis and modeling, statistics, andfinance. With recent improvements in computing and in optimizationtheory, convex minimization is nearly as straightforward as linearprogramming. These results are used by the theory of convex minimizationalong with geometric notions from functional analysis such as theHilbert projection theorem, the separating hyperplane theorem, andFarkas' lemma.