2019-02-16T11:51:49Z
http://bims.iranjournals.ir/?_action=export&rf=summon&issue=88
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Bulletin of the Iranian Mathematical Society
2016
12
18
http://bims.iranjournals.ir/article_883_e87690426a43074505de6f5bba60c5fa.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Operations Research and Optimization Conference (ORO2013)
D. T.
Luc
N.
Mahdavi-Amiri
J.-E.
Martínez-Legaz
M.
Soleimani-damaneh
2016
12
18
1
3
http://bims.iranjournals.ir/article_884_fdbf499286fb27fde043340548ce667c.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Optimality conditions for approximate solutions of vector optimization problems with variable ordering structures
B.
Soleimani
C.
Tammer
We consider nonconvex vector optimization problems with variable ordering structures in Banach spaces. Under certain boundedness and continuity properties we present necessary conditions for approximate solutions of these problems. Using a generic approach to subdifferentials we derive necessary conditions for approximate minimizers and approximately minimal solutions of vector optimization problems with variable ordering structures applying nonlinear separating functionals and Ekeland's variational principle.
Nonconvex vector optimization
variable ordering structure
Ekeland's variational principle
optimality conditions
2016
12
18
5
23
http://bims.iranjournals.ir/article_885_c784a05612ed4dd1924879eb1e344219.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Convergence in a sequential two stages decision making process
J.-E.
Martinez-Legaz
A.
Soubeyran
We analyze a sequential decision making process, in which at each step the decision is made in two stages. In the rst stage a partially optimal action is chosen, which allows the decision maker to learn how to improve it under the new environment. We show how inertia (cost of changing) may lead the process to converge to a routine where no further changes are made. We illustrate our scheme with some economic models.
sequential decision making
costs to change
convergence
2016
12
18
25
29
http://bims.iranjournals.ir/article_891_aa887782f9e8dec28eb98d0f4096894b.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Maximal elements of sub-topical functions with applications to global optimization
A. R.
Doagooei
We study the support sets of sub-topical functions and investigate their maximal elements in order to establish a necessary and sufficient condition for the global minimum of the difference of two sub-topical functions.
Global optimization
abstract convexity
sub-topical functions
Toland-Singer formula
support set
subdifferential
2016
12
18
31
41
http://bims.iranjournals.ir/article_886_4fe91887a3d2b52c52e5616a296a4306.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
First step immersion in interval linear programming with linear dependencies
M.
Hladík
M.
Černý
We consider a linear programming problem in a general form and suppose that all coefficients may vary in some prescribed intervals. Contrary to classical models, where parameters can attain any value from the interval domains independently, we study problems with linear dependencies between the parameters. We present a class of problems that are easily solved by reduction to the classical case. In contrast, we also show a class of problems with very simple dependencies, which appear to be hard to deal with. We also point out some interesting open problems.
Linear programming
interval analysis
linear dependencies
2016
12
18
43
53
http://bims.iranjournals.ir/article_887_4300ceea6463c7bf9f165fe17ce6a1be.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
An improved infeasible interior-point method for symmetric cone linear complementarity problem
N.
Mahdavi-Amiri
B.
Kheirfam
We present an improved version of a full Nesterov-Todd step infeasible interior-point method for linear complementarityproblem over symmetric cone (Bull. Iranian Math. Soc., 40(3), 541-564, (2014)). In the earlier version, each iteration consisted of one so-called feasibility step and a few -at most three - centering steps. Here, each iteration consists of only a feasibility step. Thus, the new algorithm demands less work in each iteration and admits a simple analysis of complexity bound. The complexity result coincides with the best-known iteration bound for infeasible interior-point methods.
Linear complementarity problem
infeasible interior-point method
symmetric cones
polynomial complexity
2016
12
18
55
66
http://bims.iranjournals.ir/article_888_0bb2cab455dc1ea47c66760ccbcc507d.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Solving multiobjective linear programming problems using ball center of polytopes
M. A.
Yaghoobi
A.
H. Dehmiry
Here, we aim to develop a new algorithm for solving a multiobjective linear programming problem. The algorithm is to obtain a solution which approximately meets the decision maker's preferences. It is proved that the proposed algorithm always converges to a weak efficient solution and at times converges to an efficient solution. Numerical examples and a simulation study are used to illustrate the performance of the proposed algorithm.
Multiobjective linear programming
Eficiency
Polytope
Ball center of a polytope
Target value
2016
12
18
67
88
http://bims.iranjournals.ir/article_889_04ae0492e69eaee30394f8792924760f.pdf
Bulletin of the Iranian Mathematical Society
BIMS
1017-060X
1017-060X
2016
42
Issue 7 (Special Issue)
Restricting the parameter set of the Pascoletti-Serafini scalarization
K.
Khaledian
A common approach to determine efficient solutions of a multiple objective optimization problem is reformulating it to a parameter dependent scalar optimization problem. This reformulation is called scalarization approach. Here, a well-known scalarization approach named Pascoletti-Serafini scalarization is considered. First, some difficulties of this scalarization are discussed and then removed by restricting the parameter set. A method is presented to convert a space ordered by a specific ordering cone to an equivalent space ordered by the natural ordering cone. Utilizing the presented conversion, all confirmed results and theorems for multiple objective optimization problems ordered by the natural ordering cone can be extended to multiple objective optimization problems ordered by specific ordering cones.
Multiple objective optimization
Pascoletti-Serafini scalarization
ordering cone
parameter set restriction
convexification
2016
12
18
89
112
http://bims.iranjournals.ir/article_890_cf164ddd32e9f539c5e38f02ae33a0bf.pdf