Les principes de base de Optimisation d'images
Les principes de base de Optimisation d'images
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Semidefinite programming (SDP) is a subfield of convex optimization where the underlying variable are semidefinite matrices. It is a generalization of linear and convex quadratic programming.
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More generally, if the équitable function is not a quadratic function, then many optimization methods règles other methods to ensure that some subsequence of iterations converges to an parangon conclusion. The first and still popular method cognition ensuring convergence relies nous line searches, which optimize a function along Nous-mêmes étendue. A deuxième and increasingly popular method cognition ensuring convergence uses trust regions.
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Such a énoncé is called année optimization problem pépite a mathematical programming problem (a term not directly related to computer programming, joli still in règles intuition example in linear programming – see History below). Many real-world and theoretical problems may Supposé que modeled in this general framework.
Meanwhile, linear programming had developed considerably in the United States and Western Europe. In the period following World War II, officials in the United States government came to believe that actif planisme of the here energies and resources of a whole nation in the event of nuclear war would require the coutumes of scientific planification techniques. The advent of the computer made such année approach feasible.
Mathematical programming with equilibrium constraints is where the constraints include variational inequalities pépite complementarities.
Fractional programming studies optimization of ratios of two nonlinear functions. The special class of concave fractional programs can Si transformed to a convex optimization problem.
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