convex optimization mathematical function (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. nach der Bestellung gedruckt Neuware - Printed after ordering - Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In mathematical optimization, the Rosenbrock function is a non-convex function used as a performance test problem for optimization algorithms. It is also known as Rosenbrock''s valley or Rosenbrock''s banana function. The global minimum is inside a long, narrow, parabolic shaped flat valley. To find the valley is trivial. To converge to the global minimum, however, is difficult. It is defined by f(x, y) = (1-x)^2 + 100(y-x^2)^2 .quad. It has a global minimum at (x,y) = (1,1) where f(x,y) = 0. A different coefficient of the second term is sometimes given, but this does not affect the position of the global minimum. Two variants are commonly encountered. One is the sum of N / 2 uncoupled 2D Rosenbrock problems, f(x_1, x_2, dots, x_N) = sum_{i=1}^{N/2} left[100(x_{2i-1}^2 - x_{2i})^2 + (x_{2i-1} - 1)^2 right]. This variant is only defined for even N and has predictably simple solutions. A more involved variant is f(x) = sum_{i=1}^{N-1} left[ (1-x_i)^2+ 100 (x_{i+1} - x_i^2 )^2 right] quad forall xinmathbb{R}^N.

Taschenbuch. Condizione: Neu. Rosenbrock Function | Mathematical Optimization, Convex Function, Algorithm, Optimization, Sturm's Theorem | Lambert M. Surhone (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786131256035 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand.

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.