This paper presents a logarithmic barrier method for solving a semi-definite linear program. The descent direction is the classical Newton direction. We propose alternative ways to determine the step-size along the direction which are more efficient than classical linesearches.
This paper presents a feasible primal algorithm for linear semidefinite programming. The algorithm starts with a strictly feasible solution, but in case where no such a solution is known, an application of the algorithm to an associate problem allows to obtain one. Finally, we present some numerical experiments which show that the algorithm works properly.
This paper is concerned with an algorithm proposed by Alizadeh for linear semidefinite programming. The proof of convergence given by Alizadeh relies on a wrong inequality, we correct the proof. At each step, the algorithm uses a line search. To be efficient, such a line search needs the value of the derivative, we provide this value. Finally, a few numerical examples are treated.Keywords: semidefinite programming; interior point methods.
ResumoEste artigo considera um algoritmo proposto por Alizadeh para programação semidefinida linear. A prova de convergência apresentada por Alizadeh baseia-se numa inequação errada, corrigimos a demonstração. Em cada passo, o algoritmo utiliza uma busca linear. Para ser eficiente, esta busca linear precisa do valor da derivada, apresentamos este valor. Finalmente, alguns exemplos numéricos são tratados.Palavras-chave: programação semidefinida, métodos de pontos interiores.
This paper presents a variant of logarithmic penalty methods for nonlinear convex programming. If the descent direction is obtained through a classical Newton-type method, the line search is done on a majorant function. Numerical tests show the efficiency of this approach versus classical line searches.
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