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Showing 7 results for Complexity
Mansouri, Siyavash, Zangiabadi,
Volume 3, Issue 1 (4-2012)
Abstract
We present a new algorithm obtained by changing the search directions in the algorithm given in [8]. This algorithm is based on a new technique for finding the search direction and the strategy of the central path. At each iteration, we use only the full Nesterov-Todd (NT)step. Moreover, we obtain the currently best known iteration bound for the infeasible interior-point algorithms with full NT steps, namely O(nlogn/e) , which is as good as the linear analogue.
Bai, Lesaja, Mansouri, Roos, Zangiabadi,
Volume 3, Issue 2 (9-2012)
Abstract
Many efficient interior-point methods (IPMs) are based on the use of a self-concordant barrier function for the domain of the problem that has to be solved. Recently, a wide class of new barrier functions has been introduced in which the functions are not self-concordant, but despite this fact give rise to efficient IPMs. Here, we introduce the notion of locally self-concordant barrier functions and we prove that the new barrier functions are locally self-concordant. In many cases, the (local) complexity numbers of the new barrier functions along the central path are better than the complexity number of the logarithmic barrier function by a factor between 0.5 and 1.
Dr. Behrouz Kheirfam,
Volume 4, Issue 1 (5-2013)
Abstract
We present a new full Nesterov and Todd step infeasible interior-point algorithm for semi-definite optimization. The algorithm decreases the duality gap and the feasibility residuals at the same rate. In the algorithm, we construct strictly feasible iterates for a sequence of perturbations of the given problem and its dual problem. Every main iteration of the algorithm consists of a feasibility step and some centering steps. We show that the algorithm converges and finds an approximate solution in polynomial time. A numerical study is made for the numerical performance. Finally, a comparison of the obtained results with those by other existing algorithms is made.
M Aman, J Tayyebi,
Volume 5, Issue 2 (10-2014)
Abstract
Given an instance of the minimum cost flow problem, a version of the corresponding inverse problem, called the capacity inverse problem, is to modify the upper and lower bounds on arc flows as little as possible so that a given feasible flow becomes optimal to the modified minimum cost flow problem. The modifications can be measured by different distances. In this article, we consider the capacity inverse problem under the bottleneck-type and the sum-type weighted Hamming distances. In the bottleneck-type case, the binary search technique is applied to present an algorithm for solving the problem in O(nm log n) time. In the sum-type case, it is shown that the inverse problem is strongly NP-hard even on bipartite networks
Dr. Behrouz Kheirfam,
Volume 6, Issue 2 (9-2015)
Abstract
In this paper, we propose an arc-search corrector-predictor
interior-point method for solving $P_*(kappa)$-linear
complementarity problems. The proposed algorithm searches the
optimizers along an ellipse that is an approximation of the central
path. The algorithm generates a sequence of iterates in the wide
neighborhood of central path introduced by Ai and Zhang. The
algorithm does not depend on the handicap $kappa$ of the problem,
so that it can be used for any $P_*(kappa)$-linear complementarity
problem. Based on the ellipse approximation of the central path and
the wide neighborhood, we show that the proposed algorithm has
$O((1+kappa)sqrt{n}L)$ iteration complexity, the best-known
iteration complexity obtained so far by any interior-point method
for solving $P_*(kappa)$-linear complementarity problems.
Dr. Bijan Mohammadi,
Volume 10, Issue 2 (9-2019)
Abstract
This contribution gathers some of the ingredients presented during the Iranian Operational Research community gathering in Babolsar in 2019.It is a collection of several previous publications on how to set up an uncertainty quantification (UQ) cascade with ingredients of growing computational complexity for both forward and reverse uncertainty propagation.
Dr Asadollah Alirezaei, Dr Hoda Moradi,
Volume 14, Issue 1 (6-2023)
Abstract
As one of the main challenges of the 21st century supply chain, complexity has become a crisis, costly, and difficult for many supply chain managers. However, few studies have prioritized this strategy. This study aimed to investigate the effect of supply chain complexity on competitiveness performance in the Iran Khodro Company. To achieve this goal, the relationship between different sources of complexity (upstream complexity, domestic production, and downstream complexity) and competitiveness performance is examined using a conceptual model. The statistical population of this study included all Iran Khodro experts, whose number reached 218 in 2022. Owing to the limitations of the statistical community, all members were selected as a sample. The collection tools for this study included two standard questionnaires: the supply chain and Porter's competitiveness performance. The content validity of the questionnaires was confirmed by experts, its construct validity was confirmed by confirmatory factor analysis, and the reliability of the questionnaires was confirmed using Cronbach's alpha coefficient and the combined reliability index. The collected data were analyzed using the structural equations in the LISREL software. The results of hypothesis testing show that, in general, supply chain complexity has a significant inverse effect on competitiveness performance.
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