Abstract
In this paper, we study robust necessary optimality conditions for a nondifferentiable complex fractional programming with uncertain data. A robust counterpart of uncertain complex fractional programming is introduced in the worst-case scenario. The concept of robust optimal solution of the uncertain complex fractional programming is introduced by using robust counterpart. We give an equivalence between the optimal solutions of the robust counterpart and a minimax nonfractional parametric programming. Finally, Fritz John-type and Karush–Kuhn–Tucker-type robust necessary optimality conditions of the uncertain complex fractional programming are established under some suitable conditions.
Keywords: Robust necessary optimality conditions, Uncertain complex fractional programming, Robust counterpart, Robust constraint qualification
Introduction
The linear programming and linear fractional programming in the setting of complex spaces were first studied by Levinson [22] and Swarup and Sharma [30], respectively. Subsequently, optimality conditions and duality of various complex programming including nonlinear fractional or nonfractional programming were extensively studied; see, e.g., [7, 8, 16–19]. In [26], Mond and Craven pointed out that many existed complex nonlinear programming problems are special cases of a complex programming problem whose objective function includes the square root of a quadratic form. Though the complex programming can be equivalently expressed as a real-valued bi-objective fractional programming, the solution concept of bi-objective fractional programming depends on some special partial order. However, it is not easy to choose the best suitable partial order of bi-objective fractional programming such that the solution of bi-objective fractional programming is that of complex programming. So, it is deserved to study complex programming directly. Besides, complex programming plays an important role in the field of electrical engineering, and it has also been applied to phase recovery, MaxCut, statistical signal processing, blind deconvolution, blind equalization, maximal kurtosis and minimal entropy; see, e.g., [4, 9, 13, 18, 31, 32].
In 2005, Chen et al. [4] studied complex fractional programming by using Charnes–Cooper transformation and established an equivalence between the complex fractional programming and nonfractional programming. Inspired by [8], Lai et al. [21] introduced minimax complex fractional programming and studied the Kuhn–Tucker-type necessary optimality conditions, sufficient optimality conditions as well as weak (strong and strict converse) duality results for such programming under the generalized convexity conditions. Thereafter, Lai and Huang [16, 17] considered the optimality conditions for nondifferentiable minimax fractional and nonfractional programming with complex variables. It is worth mentioning that the multipliers corresponding to the constrained functions in the necessary optimality conditions presented in [16, 17, 21] are required to be nonzero. For this reason, the obtained necessary optimality conditions [16, 17, 21] may not recover the existing necessary optimality conditions of nonlinear programming with strict inequality constraints. In addition, the minimax fractional programming and minimax nonfractional programming with complex variables can be regarded as the robust counterpart of complex fractional programming and nonfractional programming with respect to the uncertain parameters. As a matter of fact, the real-world problems are always affected by the uncertainty of data due to the prediction errors, measurement errors, the lack of complete information and major emergency (e.g., COVID-19). So, it is necessary to construct mathematical modeling with possible uncertain data to solve practical problems.
Robust optimization method is an important approach to deal with mathematical programming with uncertain data. It is based on the principle that the robust counterpart, which is also called robust optimization, of the uncertain programming has a feasible solution, where the uncertain constraints are forced to be satisfied for all possible parameter realizations within some uncertain sets. In 1937, Soyster [28] proposed a linear optimization model to construct a feasible solution for all data belonging to a convex set. It was the first step in the direction of robust optimization and the deterministic robust correspondence model given in the worst-case scenario. Recently, various robust optimization problems, such as robust linear optimization, robust quadratic optimization, robust semidefinite optimization, robust multistage optimization and robust fractional programming, are studied; see, e.g., [1–3, 5, 6, 12, 15, 23, 29]. To the best of our knowledge, there is no result on robust counterpart of complex fractional programming with uncertain data. Also, many practical problems, such as phase recovery, MaxCut, statistical signal processing, the currents and voltages of electrical networks, are always subject of uncertainty from calculation errors, incomplete information, the natural and social factors such as the extreme weather, earthquake, tsunami and social insurrection. Therefore, it is necessary and meaningful to investigate the complex fractional programming with uncertain data by the robust optimization method.
The present paper is organized as follows. In Sect. 2, we present some basic definitions, existing results as well as complex fractional programming with uncertain data. In Sect. 3, we give a minimax nonfractional parametric programming reformation for the robust counterpart of uncertain complex fractional programming and present the equivalence between optimal solutions of the robust counterpart and one of the minimax nonfractional parametric programming. In Sect. 4, Fritz John-type robust necessary optimality conditions and Karush–Kuhn–Tucker-type robust necessary optimality conditions for the robust optimal solution of uncertain complex fractional programming are established in both differentiable and nondifferentiable cases, respectively. The presented necessary optimality conditions improve the corresponding results in [16, 17, 21].
Preliminaries
Let be the n-dimensional vector space of complex numbers with inner product is defined by for all , where is the conjugate transpose of y. Denote by the set of all complex matrices. The transpose, conjugate and conjugate transpose of a matrix are denoted by , and , respectively. Set , where denotes the conjugate of . Clearly, Q is a closed convex cone; see, e.g., [11]. A matrix A is called Hermitian iff ; it is called positive semidefinite iff all of its eigenvalues are absolutely positive. For a complex number , we denote the real part and the imaginary part of z by and , respectively. For a polyhedral cone with , the dual cone of S is defined as . Clearly, . A convex subset is said to be a base of the polyhedral cone S if and only if and , where is the closure of D. In particular, D is called a compact base of S iff it is a base of S and compact set.
In this paper, we consider the following complex fractional programming with uncertain data:
| UCFP |
where are positive semidefinite Hermitian matrices, is a polyhedral cone which is specified by , , , are uncertain parameters, the uncertain subsets U, V and W of are nonempty and compact, and are continuous with respect to the second argument, and , and are analytic at each . We denote the feasible solutions set of (UCFP) by .
If f and g are analytic with respect to the first argument, the problem (UCFP) is also nondifferentiable when either or vanishes at some point with or , because the term or is nondifferentiable in the neighborhood of .
Throughout this paper, we assume that for each and , and . We adopt the robust optimization method to deal with (UCFP) in the worst-case scenarios. The robust counterpart of (UCFP) can be formulated as
| RCFP |
The feasible solution set of (RCFP) is denoted by
which is called the robust feasible set of (UCFP). Since h is analytic with respect to the first argument, is closed for each and so, is closed. A point is called a robust optimal solution of (UCFP) iff it is an optimal solution of (RCFP):
Observed that the problem (RCFP) is nondifferentiable if either or vanishes at some point with or , since the term or is nondifferentiable in the neighborhood of .
We now recall some definitions and basic results which will be used in the sequel.
Definition 2.1
[27] Let . The set is defined to be the intersection of those closed half spaces which determines S and include in their boundaries or, equivalently,
where is an arbitrary submatrix of and .
Clearly, for each . In particular, when . This implies that , where is the dual cone of .
Lemma 2.1
[20] Let and the mapping and be analytic at each . Then, for ,
where means the norm of complex vector ,
and
Lemma 2.2
Let and be analytic at , be a positive semidefinite Hermitian matrix, and and be uncertain parameters. For each , , if the function
is differentiable at , then
Proof
Noted that and . Since is differentiable at , then
From Definition 2.1, it follows that
For any , and so,
| 1 |
Therefore, one has
where the third equality follows from (1), and implies the second and fourth equalities, .
Remark 2.1
If h is uncertain-free with respect to the uncertain parameter , that is, for any , , then Lemma 2.2 reduces to Lemma 2 of [17].
Lemma 2.3
[24] Let , , and . Then the following assertions are equivalent:
- The system
has a solution . If for , then .
Lemma 2.4
[25] Let and . Then .
Lemma 2.5
[24] Let and . Then the following generalized Schwarz inequality in complex spaces holds:
and the equality holds whenever or for .
In the rest of the paper, we assume that all complex functions f, g and h are defined on the linear manifold over the real field , and the robust feasible solution set F is nonempty.
Nonfractional Parametric Programming Reformulation of (RCFP)
In this section, we present an equivalent nonfractional parametric programming problem of (RCFP) and establish the relationship between the optimal solution of the nonfractional parametric programming problem and the robust optimal solution of (UCFP).
We introduce the following nonfractional parametric programming problem:
where the parameter v is defined as
Since and for each and , one can conclude that . Since and are continuous, and U and V are compact sets, for each , there exist and such that
For the sake of brevity, for each , we set
and
It is easy to see that and are compact subsets of .
The next result shows the equivalence between the robust optimal solution of (UCFP) and ().
Theorem 3.1
Proof
(a) Let be a robust optimal solution of (UCFP) with optimal value
Then, one has
which yields that for all , . Consequently, we have
Since , are analytic on the closed linear manifold Q for any , F is closed and , we can assume that there exists such that
| 2 |
Let us show that . Suppose by contradiction that
So, for all , . Then
which contradicts the fact that is the optimal value of (UCFP) at the robust optimal solution . This together with (2) implies that
Therefore, is an optimal solution of () with and optimal value 0.
Conversely, assume that is an optimal solution of () with and optimal value 0. Then, one has
| 3 |
Since and are continuous, U and V are compact sets, then there exist and such that
Since , we have
| 4 |
We next show that
| 5 |
If (5) does not hold, then (4) implies
Since and are continuous, and U and V are compact sets, there exist and such that
and so, , which contradicts (3). So, (5) holds.
We claim that
If the above equality does not hold, then (5) implies that
Since and are analytic on the closed linear manifold Q for any , and is closed, we can assume that there exists such that
This implies that
So, for all . This together with the continuity of f and g and compactness of U and V yields that
which contradicts that
Consequently, we have
So, is the robust optimal solution of (UCFP) with optimal value .
(b) It directly follows from (a) that .
Remark 3.1
If f and g are perturbed by the same uncertain parameter, h is uncertain-free and , then Theorem 4.5 reduces Theorem 1 in [16, p. 233]. Moreover, if , and h is uncertain-free, then Theorem 4.5 reduces Lemma 2.2 in [21, p. 177]. In particular, if f, g and h are uncertain-free, f and g are also continuous and real-valued functions, and F is a compact and connected subset of , then the Dinkelbach’s result [10, Theorem, p.494] can be recovered from Theorem 3.1 (a).
Robust Necessary Optimality Conditions of (UCFP)
In this section, we study the Fritz John-type/Karush–Kuhn–Tucker-type robust necessary optimality conditions for the robust optimal solution of (UCFP) in both differentiable and nondifferentiable cases.
We first give the Fritz John-type robust necessary optimality conditions for the robust optimal solution of (UCFP) in the differentiable case.
Theorem 4.1
Let be a robust optimal solution of (UCFP) with optimal value , and . Then there exist , , and such that
| 6 |
| 7 |
| 8 |
Proof
It follows from Theorem 3.1 (a) that is an optimal solution of () with and optimal value 0. For each , and , , and are analytic at each , and A, B are positive semidefinite Hermitian matrices; we deduce that for each , and are analytic at . We observe that
where . So, () with is equivalent to the following optimization problem:
| 9 |
where . Since h is analytic on Q with respect to the first argument, and W and are nonempty compact sets, for each , there exists such that , is differentiable at and so,
| 10 |
We define the generalized Lagrangian function of the problem (9) as follows:
where and . Since U and V are compact, for each , there exist and such that
Since is an optimal solution of () with , is also an optimal solution of (9). This together with (10) yields that there exist , , , and such that
and . Since and , we set and . Then, it shows that
Moreover, we have
and , as required.
Remark 4.1
- Since , for each , is a trivial solution of the problem: , that is, , where
If , then there exist such that due to . This yields that . In Theorem 4.1, if and , then one can easily check that (6)–(8) still hold for all . In this case, for any , , and also satisfy (11)–(13), where . In order to avoid the trivial case , let us analyze the set .- (i)
- (ii)
The following Fritz John-type robust necessary optimality conditions for the robust optimal solution of (UCFP) can be obtained directly from Theorem 4.1 and Remark 4.1(b)(ii).
Theorem 4.2
Let be a robust optimal solution of (UCFP) with optimal value , , and . Then there exist , and such that
| 14 |
| 15 |
| 16 |
We next present another Fritz John-type robust necessary optimality conditions for the robust optimal solution of (UCFP) when has a compact base.
Theorem 4.3
Let be a robust optimal solution of (UCFP) with optimal value , have a compact base, and let and . Then there exist , and such that (14)–(16) hold.
Proof
Since has a compact base, we assume that is a compact base of . Then
Consequently, () with is equivalent to the following optimization problem:
| 17 |
where . Since f, g and h are analytic on Q with respect to the first argument, and U, V, W and are nonempty compact sets, for each , there exist and such that
and so, and are differentiable at . Moreover, one has
| 18 |
Since is an optimal solution of () with , is also an optimal solution of (17). This together with (18) yields that there exist , , , and such that
and . Since and is a compact base of , then . Using yields . Set , and . By the similar proof of Theorem 4.1, one can conclude that (14)–(16) hold.
We now introduce the robust constraint qualification.
Definition 4.1
The problem (UCFP) satisfies the robust constraint qualification (shortly, RCQ) at if and only if for any nonzero and for any ,
It is easy to verify that if (UCFP) satisfies RCQ at , then
and for any , implies .
If and h is uncertain-free with respect to the uncertain parameter , then RCQ reduces to the constraint qualification defined by Definition 3 in [17].
It is known that the RCQ corresponds to quasinormality condition [14] is weaker than Mangasarian–Fromovitz constraint qualification as well as positively linearly independent constraint qualification in nonlinear programming.
If and is differentiable and uncertain-free, then RCQ reduces to the following positively linearly independent constraint qualification (in short, PLICQ): For a feasible point with , there exists no nonzero such that .
For a feasible point , RCQ is slightly weaker than the following quasinormality condition: There exist no nonzero and no sequence such that and for all k, for all i with .
We present the Karush–Kuhn–Tucker-type robust necessary optimality conditions for (UCFP) by using RCQ.
Theorem 4.4
Let be a robust optimal solution of (UCFP) with optimal value . Assume that (UCFP) satisfies RCQ at with and . If or has a compact base, then there exist , and such that
| 19 |
| 20 |
| 21 |
| 22 |
Proof
It follows from Theorems 4.2 and 4.3 that there exist , and such that (21), (22) hold and
| 23 |
and .
If in (23), then
| 24 |
Since (UCFP) satisfies RCQ at , then (24) implies , which contradicts the fact that . So, . Divided both the sides of equation (23) by and set , we have
and . Consequently, (19) and (20) hold.
We next give another form of Karush–Kuhn–Tucker-type robust necessary optimality conditions for (UCFP) by using RCQ.
Theorem 4.5
Let be a robust optimal solution of (UCFP) with optimal value . Assume that (UCFP) satisfies RCQ at with and . If or has a compact base, then there exist , , and a positive integer k such that
-
(i)
, ;
-
(ii)there exist multipliers for with such that
25 26 27 28
Proof
It follows from Theorem 3.1 that is an optimal solution of () with and optimal value 0, and . For each , and , , and are analytic at each , and A, B are positive definite Hermitian matrices; we deduce that for a nonzero vector , and are analytic at . As in the proof of Theorem 4.1, we conclude from the compactness of U and V and Remark 4.1 (b)(ii) that for the robust optimal solution , there exist , , and a positive integer k with and , such that
Since , we have . Since (UCFP) satisfies RCQ at , as in the proof of Theorem 4.4, we have . Set . Then
Remark 4.2
Generally, the multipliers corresponding to the constrained conditions are not necessary to be nonzero. In Theorem 4.5, we do not require the multiplier corresponding to constrained function h to be a nonzero . So, Theorem 4.5 is distinct from Theorem 3.1 in [21, p.177] even if f, g are perturbed by the same uncertain parameter, h is uncertain-free, and , and Theorem 2 in [16, p. 234] even though f, g are perturbed by the same uncertain parameter, h is uncertain-free and . Moreover, if h is uncertain-free with respect to the uncertain parameter and , then Theorem 4.5 is different from Theorem 1 in [17, p. 1207].
We consider the robust necessary optimality conditions for the robust optimal solution of (UCFP) when or , i.e., or is nondifferentiable at . By the compactness of , one can verify that Theorem 4.5 (a) and (b) are satisfied. Let , be the same as in Theorem 4.5. Since W is a nonempty and compact set, there exists such that
Set . Motivated by the works [16, 17, 27], we introduce a subset as follows:
where the conditions (c1), (c2) and (c3) are defined as:
, if and ;
, if and ;
, if and .
We next present Karush–Kuhn–Tucker-type robust necessary optimality conditions for (UCFP) without the assumptions , as well as the assumptions that and has a compact base.
Theorem 4.6
Let be a robust optimal solution of (UCFP) with optimal value . Assume that (UCFP) satisfies RCQ at and . Then there exist , such that
| 29 |
| 30 |
| 31 |
| 32 |
Proof
Observe that , , where and
We split the proof into three cases.
(1) If and , then we deduce from that for and ,
It can be equivalently reformulated as
| 33 |
Note that . By Lemma 2.4, it implies that there exists such that and so, . Hence, we conclude from that and
Set , , , in Lemma 2.3. It follows from Lemma 2.3(a) that there exists such that , and
| 34 |
Set . Then (34) implies that
and so,
| 35 |
| 36 |
Clearly, (36) can be equivalently expressed as
| 37 |
Since is positive semidefinite Hermitian matrix, we have . Due to , set . So, (29) is obtained by summing (35) and (37). By the same proof as in (27), (31) can be obtained.
Owing to and , we obtain . It follows from Lemma 2.5 and that for , . Taking in the above equality, one has and so, . Since B is positive semidefinite Hermitian matrix, then . It therefore shows that .
(2) If and , then the desired results can be deduced by the similar argument as in the proof of case (1), and so, it is omitted.
(3) If and , then by the similar argument as in the proof of [16, Theorem 3, pp. 235-237], one can obtain the desired results. Altogether, (29)–(32) hold.
Remark 4.3
Generally, the multipliers corresponding to the constrained conditions are not necessary to be nonzero. In Theorem 4.6, we do not require the multiplier corresponding to constrained function h to be a nonzero . So, Theorem 4.6 is distinct from Theorem 3 in [16, p. 235] when f, g are perturbed by the same uncertain parameter, h is uncertain-free and , and Theorem 6 in [17, p. 1207] when h is uncertain-free with respect to the uncertain parameter and .
In particular, if we set in Theorem 4.6, then the following robust necessary optimality conditions for (UCFP) holds.
Corollary 4.1
Let be a robust optimal solution of (UCFP) with optimal value . Assume that (UCFP) satisfies RCQ at and . Then there exist , , , and such that
Conclusions
A complex fractional programming with uncertain data is studied by the robust optimization method. The robust counterpart of (UCFP) is introduced, and then, a minimax nonfractional parametric programming reformation for the robust counterpart is presented. The equivalence between the optimal solutions of the robust counterpart and one of the minimax nonfractional parametric programming is also derived. Fritz John-type robust necessary optimality conditions for the robust optimal solution of (UCFP) are established under the assumption that has a compact base or, . By the RCQ, Karush–Kuhn–Tucker-type robust necessary optimality conditions for the robust optimal solution of (UCFP) are also obtained under some suitable assumptions. The presented necessary optimality conditions improve the corresponding results in [16, 17, 21].
For the future research, it is interesting to investigate the robust sufficient optimality conditions, duality, stability, robust radius and algorithm of (UCFP) by the necessary optimality conditions presented in this paper. It is well known that a cone has a base if and only if its strict dual cone or the quasi-interior of its dual cone is nonempty. However, there are no sufficient conditions for ensuring a cone having a compact base. So, it is also deserved to study the sufficient conditions for a cone having a compact base.
Acknowledgements
The authors are grateful to the anonymous referees for their valuable comments and suggestions, which help to improve the paper. They also would like to thank Dr. Tone-Yau Huang for his valuable discussion in the preparation of this paper. This research was partially supported by the Fundamental Research Funds for the Central Universities (XDJK2020B048), the Natural Science Foundation of China (12071379, 11771058, 11871383, 61673006), the Hubei Provincial Natural Science Foundation for Distinguished Young Scholars (2019CFA088) and Basic Research Grant (SB191054) of KFUPM.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Contributor Information
Jiawei Chen, Email: J.W.Chen713@163.com.
Suliman Al-Homidan, Email: homidan@kfupm.edu.sa.
Qamrul Hasan Ansari, Email: qhansari@gmail.com.
Jun Li, Email: lijunmath123@163.com.
Yibing Lv, Email: Yibinglv@yangtzeu.edu.cn.
References
- 1.Ben-Tal A, Nemirovski A. Robust convex optimization. Math. Oper. Res. 1998;23:769–805. doi: 10.1287/moor.23.4.769. [DOI] [Google Scholar]
- 2.Ben-Tal A, Nemirovski A. Robust solutions of linear programming problems contaminated with uncertain data. Math. Program. Ser. A. 2000;88:411–424. doi: 10.1007/PL00011380. [DOI] [Google Scholar]
- 3.Ben-Tal A, Ghaoui LE, Nemirovski A. Robust Optimization. Princeton: Princeton University Press; 2009. [Google Scholar]
- 4.Chen JC, Lai HC, Schaible S. Complex fractional programming and the Charnes-Cooper transformation. J. Optim. Theory Appl. 2005;126:203–213. doi: 10.1007/s10957-005-2669-y. [DOI] [Google Scholar]
- 5.Chen JW, Köbis E, Yao JC. Optimality conditions and duality for robust nonsmooth multiobjective optimization problems with constraints. J. Optim. Theory Appl. 2019;181:411–436. doi: 10.1007/s10957-018-1437-8. [DOI] [Google Scholar]
- 6.Chen JW, Li J, Li X, Lv Y, Yao JC. Radius of robust feasibility of system of convex inequalities with uncertain data. J. Optim. Theory Appl. 2020;184:384–399. doi: 10.1007/s10957-019-01607-7. [DOI] [Google Scholar]
- 7.Das C, Swarup K. Nonlinear complex programming with nonlinear constraints. Z. Angew. Math. Mech. 1977;57:333–338. doi: 10.1002/zamm.19770570610. [DOI] [Google Scholar]
- 8.Datta N, Bhatia D. Duality for a class of nondifferentiable mathematical programming problems in complex spaces. J. Math. Anal. Appl. 1984;101:1–11. doi: 10.1016/0022-247X(84)90053-2. [DOI] [Google Scholar]
- 9.Denoho DL. On minimum entropy deconvolution. In: Findley DF, editor. Applied Time Series Analysis II. New York: Academic Press; 1981. [Google Scholar]
- 10.Dinkelbach W. On nonlinear fractional programming. Manage. Sci. 1967;13:492–498. doi: 10.1287/mnsc.13.7.492. [DOI] [Google Scholar]
- 11.Ferrero O. On nonlinear programming in complex space. J. Math. Anal. Appl. 1992;164:399–416. doi: 10.1016/0022-247X(92)90123-U. [DOI] [Google Scholar]
- 12.Gorissen BL. Robust fractional programming. J. Optim. Theory Appl. 2015;166:508–528. doi: 10.1007/s10957-014-0633-4. [DOI] [Google Scholar]
- 13.Haykin S. Adaptive Filter Theory. Englewood Cliffs, NJ: Prentice Hall; 1996. [Google Scholar]
- 14.Hestenes M. Optimization Theory. New York: The Finite Dimensional Case. John Wiley & Sons; 1975. [Google Scholar]
- 15.Jeyakumar V, Li GY. Robust duality for fractional programming problems with constraint-wise data uncertainty. J. Optim. Theory Appl. 2011;151:292–303. doi: 10.1007/s10957-011-9896-1. [DOI] [Google Scholar]
- 16.Lai HC, Huang TY. Optimality conditions for a nondifferentiable minimax fractional programming with complex variables. J. Math. Anal. Appl. 2009;359:229–239. doi: 10.1016/j.jmaa.2009.05.049. [DOI] [Google Scholar]
- 17.Lai HC, Huang TY. Optimality conditions for a nondifferentiable minimax programming in complex spaces. Nonlinear Anal. 2009;71:1205–1212. doi: 10.1016/j.na.2008.11.053. [DOI] [Google Scholar]
- 18.Lai HC, Liu JC. Complex fractional programming involving generalized quasi / pseudo convex functions. Z. Angew. Math. Mech. 2002;82:159–166. doi: 10.1002/1521-4001(200203)82:3<159::AID-ZAMM159>3.0.CO;2-5. [DOI] [Google Scholar]
- 19.Lai HC, Liu JC. A new characterization on optimality and duality for nondifferentiable minimax fractional programming problems. J. Nonlinear Convex Anal. 2011;12:69–80. [Google Scholar]
- 20.Lai HC, Lee JC, Ho SC. Parametric duality on minimax programming involving generalized convexity in complex space. J. Math. Anal. Appl. 2006;323:1104–1115. doi: 10.1016/j.jmaa.2005.11.026. [DOI] [Google Scholar]
- 21.Lai HC, Liu JC, Schaible S. Complex minimax fractional programming of analytic functions. J. Optim. Theory Appl. 2008;137:171–184. doi: 10.1007/s10957-007-9332-8. [DOI] [Google Scholar]
- 22.Levinson N. Linear programming in complex space. J. Math. Anal. Appl. 1966;14:44–62. doi: 10.1016/0022-247X(66)90061-8. [DOI] [Google Scholar]
- 23.Li X, Wang Q, Lin Z. Optimality conditions and duality for minimax fractional programming problems with data uncertainty. J. Indust. Manag. Optim. 2019;15:1133–1151. [Google Scholar]
- 24.Mond B. An extension of the transposition theorems of Farkas and Eisenberg. J. Math. Anal. Appl. 1970;32:559–566. doi: 10.1016/0022-247X(70)90277-5. [DOI] [Google Scholar]
- 25.Mond B, Craven BD. A class of nondifferentiable complex programming problems. Optim. 1975;6:581–591. doi: 10.1080/02331937508842275. [DOI] [Google Scholar]
- 26.Mond B, Craven BD. A class of nondifferentiable complex programming problems. Math. Operationsforsch. Statist. 1975;6:581–591. doi: 10.1080/02331887508801238. [DOI] [Google Scholar]
- 27.Parkash O, Saxena PC, Patkar V. Nondifferentiable fractional programming in complex space. Z. Angew. Math. Mech. 1984;64:59–62. doi: 10.1002/zamm.19840640110. [DOI] [Google Scholar]
- 28.Soyster A. Convex programming with set-inclusive constraints and applications to inexact linear programming. Oper. Res. 1973;21:1154–1157. doi: 10.1287/opre.21.5.1154. [DOI] [Google Scholar]
- 29.Sun X, Teo KL, Tang L. Dual approaches to characterize robust optimal solution sets for a class of uncertain optimization problems. J. Optim. Theory Appl. 2019;182:984–1000. doi: 10.1007/s10957-019-01496-w. [DOI] [Google Scholar]
- 30.Swarup K, Sharma JC. Programming with linear fractional functionals in complex spaces. Cahiers centre d’Etudes Rech. Oper. 1970;12:103–109. [Google Scholar]
- 31.Waldspurger I, d’Aspremont A, Mallat S. Phase recovery, MaxCut and complex semidefinite programming. Math. Program. Ser. A. 2015;149:47–81. doi: 10.1007/s10107-013-0738-9. [DOI] [Google Scholar]
- 32.Wu C, Xu DC, Du DL, Xu W. An approximation algorithm for the balanced Max-3-Uncut problem using complex semidefinite programming rounding. J. Comb. Optim. 2016;32:1017–1035. doi: 10.1007/s10878-015-9880-z. [DOI] [Google Scholar]
