A feasible primal–dual interior point method for linear semidefinite programming

An interior-point algorithm for semidefinite optimization based on a new parametric kernel function

“…In this section, we give some numerical results to see some advantages of using the mentioned new kernel function. The results are obtained by performing of Algorithm1 with the proposed kernel function on some test problems given in [9,33,34,35]. Also the obtained results are compared with the one generated by other eight famous kernel function which presented in Table 1.…”

A primal-dual interior-point method for the semidefinite programming problem based on a new kernel function

“…If µ → 0, then the limit of the central path exist. Since the limit points satisfy the complementarity condition, the limit yields optimal solutions for (P) and (D) (see, e.g., [5,24]).…”

“…They not only have polynomial complexity, but also are highly efficient in practice. They motivate researchers to elaborate extensions for more general classes of optimization problems: the linear complementarity problem (LCP) [2], the convex programming problem (CP) [3] and the semidefinite programming problem (SDP) [4,5,6].…”

Proyecciones Paramétricas para el Escape de Aristas en Poliedros de Forma Ax ≤ b

“…Además, las aplicaciones de este tipo de algoritmos no se limitan a problemas PL. Por ejemplo, Klintberg y Gros (2016) recurren a uno para enfrentar problemas de control óptimo en procesos industriales, Wu et al (2016) utilizan un algoritmo de punto interior para optimización del flujo de energía eléctrica, Touil et al (2017) proponen un algoritmo primal-dual (de punto interior) para solucionar problemas de programación semidefinida. Finalmente, Dattaa et al (2017) incorporan un procedimiento de punto interior para solucionar problemas de optimización multiobjetivo en regiones no convexas.…”