DUALITY IN MULTIOBJECTIVE FRACTIONAL PROGRAMMING PROBLEMS INVOLVING (Hp, r)-INVEX FUNCTIONS†

Journal of Applied Mathematics & Informatics.
2014.
Jan,
32(1_2):
99-111

- Received : February 14, 2013
- Accepted : April 11, 2013
- Published : January 28, 2014

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In this paper, we have taken step in the direction to establish weak, strong and strict converse duality theorems for three types of dual models related to multiojective fractional programming problems involving (
H_{p}
,
r
)-invex functions.
AMS Mathematics Subject Classification : 90C32, 90C46, 49N15.
et al
.
[9]
derived duality results for a Mond-Weir type dual problem related to multiobjective fractional programming problem involving pseudolinear and
η
-pseudolinear functions. Osuna-Gómez
et al
.
[15]
focus his study to establish the optimality condition and duality theorems for a class of multiobjective fractional programs under generalized convexity assumptions by applying parametric approach.
The notion of convexity was not enough to meet the challenging demand of some problems on Economics and Engineering. To meet this demand the notion of invexity was introduced by Hanson
[7]
by substituting the linear term (
x
−
y
) appearing in the definition of convex functions with an arbitrary vector valued function.
Antczak
[2]
introduced a new class of functions named (
p
,
r
)-invex function, which is an extension of invex function. Recently, Jayswal
et al
.
[8]
focus his study on multiobjective fractional programming problems and derived sufficient optimality conditions and duality theorems involving (
p
,
r
) −
ρ
− (
η
,
θ
)-invex functions
[11]
.
Yuan
et al.
[23]
introduced new types of generalized convex functions and sets, which are called locally (
H_{p}
,
r
,
α
)−pre-invex and locally
H_{p}
-invex sets. They obtained also optimality conditions and duality theorems for a scalar nonlinear programming problem. Recently, Liu
et al.
[10]
proposed the concept of (
H_{p}
,
r
)-invex function and focus his study to discuss sufficient optimality conditions to multiple objective programming problem and multiobjective fractional programming problem involving the aforesaid class of functions but no step was taken to prove the duality results involving (
H_{p}
,
r
)-invex functions.
In this paper, viewing the importance of duality theorems in optimization theory, we establish weak, strong and strict converse duality theorems involving (
H_{p}
,
r
)-invex function to three types of dual models related to mulitiobjective fractional programming problems. The organization of the remainder of this paper is as follows. The formulation of multiobjective fractional programming problem along with some definitions and notations related to (
H_{p}
,
r
)-invexity is given in Section 2. Weak, strong and strict converse duality theorems for three types of dual models related to multiobjective fractional programming problem under (
H_{p}
,
r
)-invexity are derived in Section 3 to Section 5. Finally, conclusions and further developments are given in Section 6.
R^{n}
be the
n
-dimensional Euclidean space,
Let
x
,
y
∈
R^{n}
. Then
and
x
≠
y
.
Definition 2.1
(
[2]
). Let
a
_{1}
,
a
_{2}
> 0, λ ∈ (0, 1) and
r
∈
R
. Then the weighted
r
-mean of
a
_{1}
and
a
_{2}
is given by
Definition 2.2
(
[23]
).
X
⊂
R^{n}
is locally
H_{p}
-invex set if and only if, for any
x
,
u
∈
X
, there exist a maximum positive number
a
(
x
,
u
) ≤ 1 and a vector function
H_{p}
:
X
×
X
× [0,1] →
R^{n}
, such that
and
H_{p}
(
x
,
u
; λ) is continuous on the interval (0,
a
(
x
,
u
)), where the logarithm and the exponentials appearing in the relation are understood to be taken componentwise.
Definition 2.3
(
[23]
). A function
f
:
X
→
R
defined on a locally
H_{p}
-invex set
X
⊂
R^{n}
is said to be locally (
H_{p}
,
r
)-pre-invex on
X
if, for any
x
,
u
∈
X
, there exists a maximum positive number
a
(
x
,
u
) ≤ 1 such that
where the logarithm and the exponentials appearing in the left-hand side of the inequality are understood to be taken componentwise. If
u
is fixed, then
f
is said to be (
H_{p}
,
r
)-pre-invex at
u
. Correspondingly, if the direction of above inequality is changed to the opposite one, then
f
is said to (
H_{p}
,
r
)-pre-incave on
S
or at
u
.
For convenience, we assume that
X
be a
H_{p}
-invex set,
H_{p}
is right differentiable at 0 with respect to the variable λ for each given pair
x
,
u
∈
X
, and
f
:
X
→
R
is differential on
X
. The symbol
denotes the right derivative of
H_{p}
at 0 with respect to the variable λ for each given pair
x
,
u
∈
X
; ∇
f
(
x
) ≜ (∇
_{1}
f
(
x
), ...,∇
_{n}f
(
x
))
^{T}
denotes the differential of
f
at
x
, and so
Definition 2.4
(
[10]
). Let
X
be a
H_{p}
-invex set,
H_{p}
is right differentiable at 0 with respect to the variable λ for each given pair
x
,
u
∈
X
, and
f
:
X
→
R
is differentiable on
X
. If for all
x
∈
X
, one of the relations
hold, then
f
is said to be (
H_{p}
,
r
)-invex (strictly (
H_{p}
,
r
)-invex) at
u
∈
X
. If the above inequalities are satisfied at any point
u
∈
X
then
f
is said to be (
H_{p}
,
r
)-invex (strictly (
H_{p}
,
r
)-invex) on
X
.
We now consider the following multiobjective fractional programming problems:
(FP)
Minimize
subject to
where
f
,
g
:
X
→
R^{k}
and
h
:
X
→
R^{m}
,
f
= (
f
_{1}
,
f
_{2}
, ...,
f
_{k}
),
g
= (
g
_{1}
,
g
_{2}
, ...,
g
_{k}
),
h
= (
h
_{1}
,
h
_{2}
, ...,
h
_{m}
), are differentiable functions on a (nonempty)
H_{p}
-invex set
X
. Without loss of generality, we can assume that
f_{i}
(
x
) ≥ 0,
g_{i}
(
x
) > 0,
i
= 1, 2, ...,
k
for all
x
∈
X
. Let
X
^{0}
= {
x
∈
X
:
h
(
x
) ≤ 0} be the set of all feasible solutions to (FP).
We denote
and
ϕ
(
x
) = (
ϕ
_{1}
(
x
),
ϕ
_{2}
(
x
), ...,
ϕ_{k}
(
x
)).
Definition 2.5.
A feasible solution
x
^{∗}
∈
X
^{0}
of (FP) is said to be an efficient solution of (FP) if there exist no other feasible solution
x
∈
X
^{0}
such that
and
It is well known (see, for example
[17]
) that, if
x
^{∗}
∈
X
^{0}
is an efficient solution of a multiobjective fractional programming problem (FP), then the following necessary optimality conditions are satisfied:
Theorem 2.1
(Necessary optimality conditions).
Let x
^{∗}
∈
X
^{0}
be an efficient solution to a multiobjective fractional programming problem
(FP)
and h satisfies the constraints qualification
[17]
at x
^{∗}
.
Then, there exist
such that
where
The above conditions will be needed in the present analysis.
Remark 2.1
All the theorems in the subsequent parts of this paper will be proved only in the the case when
r
≠ 0. The proofs in other cases are easier than in this one, since the differences arise only the form of inequality. Moreover, without loss of generality, we shall assume that
r
> 0 (in the case when
r
< 0, the direction some of the inequalities in the proof of the theorems should be changed to the opposite one).
DI
) Maximize
v
= (
v
_{1}
,
v
_{2}
, ...,
v
_{k}
)
subject to
Theorem 3.1
(Weak duality).
Let x
∈
X
^{0}
be a feasible solution for
(FP),
and let
(
u
,
y
,
z
,
v
)
be a feasible solution for
(DI).
Moreover
,
we assume that any one of the following conditions holds:
Then
Proof.
If the condition (a) holds, then (
H_{p}
,
r
)-invexity of
S
(.) at
u
, we have
Using the fundamental property of exponential functions, the above inequality together with (7), imply that
Now suppose contrary to the result that
Then
That is,
The above inequalities along with (10) give
By the feasibility of x and from (9) and (10), we have
On adding (12) and (13), we obtain
i.e.,
which contradicts (11).
If condition (
b
) holds, the from the (
H_{p}
,
r
)-invexity of
Q
(.) at
u
,
equivalently
From (13) and (14), we get
The above inequality together with (7) yields
From the (
H_{p}
,
r
)-invexity of
P
(.) at
u
, we have
The inequalities (15) and (16), and the fundamental property of exponential functions imply that
That is,
Again if
then we get (12) in the same way. But (12) contradicts (17). Therefore,
This completes the proof.
Theorem 3.2
(Strong duality).
Let x
^{∗}
be an efficient solution for
(FP)
and let h satisfy the constraints qualification
[17]
at x
^{∗}
.
Then there exist y
^{∗}
∈ Ω,
z
^{∗}
∈
R^{m} and v
^{∗}
∈
R^{k} such that
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
)
is feasible for
(DI).
Also, if the weak duality theorem 3.1 holds for all feasible solutions of the problems
(FP)
and
(DI),
then
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
)
is an efficient solution for
(DI)
and the two objectives are equal at these points
.
Proof.
Since
x
^{∗}
is an efficient solution for (FP) and
h
satisfy the constraints qualification at
x
^{∗}
, there exist
y
^{∗}
∈ Ω,
z
^{∗}
∈
R^{m}
and
v
^{∗}
∈
R^{k}
such that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
) satisfies (2)-(6). This, in turn, imply that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
) is feasible for (DI). From the weak duality theorem, for any feasible points (
x
,
y
,
z
,
v
) to (DI), the inequality
holds. Hence we conclude that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
) is an efficient solution to (DI) and the objective functions of (FP) and (DI) are equal at these points. This completes the proof.
Theorem 3.3
(Strict converse duality).
Assume that x
^{∗}
and
(
u
^{∗}
,
y
^{∗}
,
z
^{∗}
,
v
^{∗}
)
be an efficient solution for
(FP)
and
(DI),
respectively with
Assume that
is strictly
(
H_{p}
,
r
)-
invex at u
^{∗}
.
Then x
^{∗}
=
u
^{∗}
;
that is
,
u
^{∗}
is an efficient solution for
(FP).
Proof.
Suppose on the contrary that
x
^{∗}
≠
u
^{∗}
. From (8), (9) and (10), we get
From the strictly (
H_{p}
,
r
)-invexity of
A
(.), we have
Using the fundamental property of exponential functions, the above inequality together with (7), imply that
Since
i.e.,
By the feasibility of
x
^{∗}
and (10), we have
Therefore, from (10), (20) and (21), we conclude that
Hence from (19) and (22), we have
A
(
u
^{∗}
) < 0 which contradicts (18). Hence
x
^{∗}
=
u
^{∗}
. This completes the proof.
Remark 3.1 The function
A
(.) in Theorem 3.3 is expressed by the sum of the modified objective part
of (FP) and its constraint part
If
B
(.) is strictly (
H_{p}
,
r
)-invex and
C
(.) is (
H_{p}
,
r
)-invex then the Theorem 3.3 is still holds.
Theorem 4.1
Let x
^{∗}
be an efficient solution to
(FP).
Assume that h satisfies the constraints qualification at x
^{∗}
.
Then there exist
such that
Now we consider the following parameter free dual problem to (FP):
(DII)
Maximize
subject to
Denote
and
Throughout this section, we assume
and
g_{i}
(
u
) > 0, for all
i
= 1, 2, ...,
k
.
Theorem 4.2
(Weak duality).
Let x
∈
X
^{0}
be a feasible solution for
(FP)
and let
(
u
,
y
,
z
)
be a feasible solution for
(DII).
Assume that
is
(
H_{p}
,
r
)-
invex at u
.
Then
Proof.
From (
H_{p}
,
r
)-invexity of Θ(.) at
u
, we have
Using the fundamental property of exponential functions, the above inequality together with (27), imply that
i.e,
Suppose contrary to the result that
Then
It follows that
equivalently,
From the feasibility of
x
,
g_{i}
(
u
) > 0 and (28), we have
Therefore (30), implies
i.e,
which contradicts (29). This completes the proof.
Theorem 4.3
(Strong duality).
Let x
^{∗}
be an efficient solution for
(FP)
and let h satisfy the constraints qualification
[17]
at x
^{∗}
.
Then there exist y
^{∗}
∈ Ω
and z
^{∗}
∈
R^{m} such that
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
)
is feasible to
(DII).
Also, If the weak duality theorem 5.2 holds for all feasible solutions of the problem
(FP)
and
(DII),
then
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
)
is an efficient solution for
(DII)
and the two objectives are equal at these points
.
Proof.
Since
x
^{∗}
is an efficient solution for (FP) and
h
satisfy the constraints qualification at
x
^{∗}
, there exist
y
^{∗}
∈ Ω and
z
∗ ∈
R^{m}
such that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
) satisfies (23)-(26). This, in turn, imply that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
) is feasible for (DII). From the weak duality theorem 4.2, for any feasible points (
x
,
y
,
z
) to (DII)), the inequality
holds. Hence we conclude that (
x
^{∗}
,
y
^{∗}
,
z
^{∗}
) is an efficient solution to (DII) and the objective functions of (FP) and (DII) are equal at these points. This completes the proof.
Theorem 4.4
(Strict converse duality).
Assume that x
^{∗}
and
(
u
^{∗}
,
y
^{∗}
,
z
^{∗}
)
be an efficient solution for
(FP)
and
(DII),
respectively
.
Assume that
is strictly
(
H_{p}
,
r
)-
invex at u
^{∗}
Then x
^{∗}
=
u
^{∗}
;
that is
,
u
^{∗}
is an efficient solution for
(FP).
Proof.
Suppose on the contrary that
x
^{∗}
≠
u
^{∗}
. From Theorem 4.3, we know that there exist
and
such that
is an efficient solution for (DII) and
By (24), (26) and (31), we obtain
Hence
From (28) and (33), we have
By the feasibility of
x
^{∗}
,
g_{i}
(
u
^{∗}
) > 0, from (28) and the above inequality, we have
Therefore,
That is,
On the other hand, from strictly (
H_{p}
,
r
)-invexity of
U
(.) at
u
^{∗}
, we have
The above inequality together with (27) and the fundamental property of the exponential functions yields
which contradicts inequality (34). Hence
x
^{∗}
=
u
^{∗}
; that is,
u
^{∗}
is an efficient solution for (FP). This completes the proof.
(DIII)
Maximize
subject to
Denote
and Փ(
u
) = (Փ
_{1}
(
u
),Փ
_{2}
(
u
), ...,Փ
_{k}
(
u
)).
Now we shall state weak, strong and strict converse duality theorems without proof as they can be proved in light of the Theorem 4.2, Theorem 4.3 and Theorem 4.4, proved in previous section.
Theorem 5.1
(Weak duality).
Let x
∈
X
^{0}
be a feasible solution for
(FP)
and let
(
u
,
y
,
z
)
be a feasible solution for
(DIII).
Assume that
is
(
H_{p}
,
r
)-
invex at u
.
Then
Theorem 5.2
(Strong duality).
Let x
^{∗}
be an efficient solution for
(FP)
and let h satisfy the constraints qualification
[17]
at x
^{∗}
.
Then there exist y
^{∗}
∈
I and z
^{∗}
∈
R^{m} such that
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
)
is feasible to
(DIII).
Also
,
If the weak duality theorem 5.1 holds for all feasible solutions of the problem
(FP)
and
(DIII),
then
(
x
^{∗}
,
y
^{∗}
,
z
^{∗}
)
is an efficient solution for
(DIII)
and the two objectives are equal at these points
.
Theorem 5.3
(Strict converse duality).
Assume that x
^{∗}
and
(
u
^{∗}
,
y
^{∗}
,
z
^{∗}
)
be an efficient solution for
(FP)
and
(DIII),
respectively
.
Assume that
is strictly
(
H_{p}
,
r
)-
invex at u
^{∗}
.
Then x
^{∗}
=
u
^{∗}
;
that is
,
u
^{∗}
is an efficient solution for
(FP).
H_{p}
,
r
)-invex functions to established duality results for three type of dual models related to multiobjective fractional programming problem. The question arise whether optimality and duality theorems established in this paper also holds under the assumption of (
H_{p}
,
r
)-invexity for a class of minimax fractional programming problem considered in
[1]
.
Anurag Jayswal received M.A. and Ph.D from Banaras Hindu University, Varanasi–221 005, U.P., India. He is currently an Assistant Professor at Indian School of Mines, Dhanbad, India since 2010. His research interests include mathematical programming, nonsmooth analysis and generalized convexity.
Department of Applied Mathematics, Indian School of Mines, Dhanbad-826 004, India.
e-mail: anurag jais123@yahoo.com
I. Ahmad received M.Sc., M.Phil and Ph. D from Indian Institute of Technology, Roorkee, India. He is an Associate Professor at Aligarh Muslim University, India. Presently, he is on leave from Aligarh Muslim University and working as an Associate Professor at King Fahd University of Petroleum and Minerals, Saudi Arabia. His major areas of research interests are Mathematical Programming and Manifolds inclding generalizations of convexity, optimality crieteria, duality theory, geodesic convexity and continous-time programming. He is author and co-author of seventy research papers on previous mentioned field, published in journals of international repute.
Department of Mathematics, King Fahd University of Petroleum and Minerals, Saudi Arabia.
e-mail: drizhar@kfupm.edu.sa , izharmaths@hotmail.com
Ashish Kumar Prasad received M.Sc. from Vinoba Bhave University, Hazaribag-835001, Jharkhand, India and M.Phil from Indian School of Mines, Dhanbad-826004, India. After that, he joined Indian School of Mines, Dhanbad-826004, India, as a research scholar and presently he is working in the area of generalized convexity.
Department of Applied Mathematics, Indian School of Mines, Dhanbad-826 004, India.
e-mail: ashishprasa@gmail.com

1. Introduction

Generalized convexity plays an important role in many aspects of optimization, such as optimality conditions, duality theorems, variational inequalities, saddle point theory and convergence of optimization algorithms, so the research on generalized convexity is one of the important aspects of mathematical programming problems.
The problem in which objective functions are ratio of two functions are termed as fractional programming problems. Such problems are studied in various fields like economics
[3]
, information theory
[12]
, heat exchange networking
[24]
and others. Duality in multiobjective fractional programming problems involving generalized convex functions have been of much interest in recent past, (see
[4
,
5
,
8
,
14
,
16
,
18
,
22]
) and the references cited therein. For more information about fractional programming problems, the reader may consult the research bibliography compiled by Stancu-Minasian
[19
,
20
,
21]
.
Mukherjee
[13]
considered a multiobjective fractional programming problem and discussed the Mond-Weir type duality results under generalized convexity. Gulati and Ahmad
[6]
proved the duality results using Fritz John conditions for multiobjective programming problem involving generalized convex functions. Kaul
2. Notation and Preliminaries

Throughout the paper, let
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3. Parametric duality

We consider the following parametric dual of (FP) as follows:
(
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- (a)
- (b)

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4. Parameter free duality

In this section, we take the following form of theorem 2.1:
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5. Mond-Weir duality

In this section, we consider the following Mond-Weir dual to (FP):
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6. Conclusion

In this paper, we have used the concept of (
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Citing 'DUALITY IN MULTIOBJECTIVE FRACTIONAL PROGRAMMING PROBLEMS INVOLVING (Hp, r)-INVEX FUNCTIONS†
'

@article{ E1MCA9_2014_v32n1_2_99}
,title={DUALITY IN MULTIOBJECTIVE FRACTIONAL PROGRAMMING PROBLEMS INVOLVING (Hp, r)-INVEX FUNCTIONS†}
,volume={1_2}
, url={http://dx.doi.org/10.14317/jami.2014.099}, DOI={10.14317/jami.2014.099}
, number= {1_2}
, journal={Journal of Applied Mathematics & Informatics}
, publisher={Korean Society of Computational and Applied Mathematics}
, author={JAYSWAL, ANURAG
and
AHMAD, I.
and
PRASAD, ASHISH KUMAR}
, year={2014}
, month={Jan}