Authors:
Derkaoui Orkia
and
Lehireche Ahmed
Affiliation:
University Djillali Liabes of Sidi Bel Abbes, Algeria
Keyword(s):
Interior-point Methods, SemiDefinite Programming, Linear Research, Primal-Dual Interior-Point Method, Predictor-corrector Procedure, HRVM/KSH/M Search Direction.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Linear Programming
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Symbolic Systems
Abstract:
This paper provides a new variant of primal-dual interior-point method for solving a SemiDefinite Program
(SDP). We use the PDIPA (primal-dual interior-point algorithm) solver entitled SDPA (SemiDefinite
Programming Algorithm). This last uses a classical Newton descent method to compute the predictor-corrector
search direction. The difficulty is in the computation of this line-search, it induces high
computational costs. Here, instead we adopt a new procedure to implement another way to determine the
step-size along the direction which is more efficient than classical line searches. This procedure consists in
the computation of the step size in order to give a significant decrease along the descent line direction with a
minimum cost. With this procedure we obtain à new variant of SDPA. The comparison of the results
obtained with the classic SDPA and our new variant is promising.