|
|
|
|
|
 |
Search published articles |
 |
|
Showing 3 results for Zangiabadi
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.
Zangiabadi, Rabie,
Volume 3, Issue 2 (9-2012)
Abstract
In today’s highly competitive
market, the pressure on organizations to find a better way to create and
deliver value to customers is mounting. The decision involves many
quantitative and qualitative factors that may be conflicting
in nature. Here, we present a new model for transportation problem with
consideration of quantitative and qualitative data. In the model, we quantify
the qualitative data by using the weight assessment technique in the fuzzy
analytic hierarchy process. Then, a preemptive fuzzy goal programming model is
formulated to solve the proposed model. The software package LINGO is used for
solving the fuzzy goal programming model. Finally, a numerical example is given
to illustrate that the proposed model may lead to a more appropriate solution.
|
|
|
|
|
|
|
|
|
