TY - JOUR
ID - 1805
TI - A path following interior-point algorithm for semidefinite optimization problem based on new kernel function
JO - Journal of Mathematical Modeling
JA - JMM
LA - en
SN - 2345-394X
AU - Djeffal, El Amir
AU - Djeffal, Lakhdar
AD - Department of Mathematics, University of Batna 2, Batna, Algeria
Y1 - 2016
PY - 2016
VL - 4
IS - 1
SP - 35
EP - 58
KW - quadratic programming
KW - convex nonlinear programming
KW - interior point methods
DO -
N2 - In this paper, we deal to obtain some new complexity results for solving semidefinite optimization (SDO) problem by interior-point methods (IPMs). We define a new proximity function for the SDO by a new kernel function. Furthermore we formulate an algorithm for a primal dual interior-point method (IPM) for the SDO by using the proximity function and give its complexity analysis, and then we show that the worst-case iteration bound for our IPM is $O(6(m+1)^{\frac{3m+4}{2(m+1)}}\Psi _{0}^{\frac{m+2}{2(m+1)}}\frac{1}{\theta }\log \frac{n\mu ^{0}}{\varepsilon })$, where $m>4$.
UR - https://jmm.guilan.ac.ir/article_1805.html
L1 - https://jmm.guilan.ac.ir/article_1805_783e5a298d09d5f817ee51668fdce93b.pdf
ER -