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. 2021 Mar 2;189(1):221–243. doi: 10.1007/s10957-021-01829-8

Robust Necessary Optimality Conditions for Nondifferentiable Complex Fractional Programming with Uncertain Data

Jiawei Chen 1, Suliman Al-Homidan 2,, Qamrul Hasan Ansari 2,3, Jun Li 1, Yibing Lv 4
PMCID: PMC7921289  PMID: 33678905

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, 1619]. 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., [13, 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 Cn be the n-dimensional vector space of complex numbers with inner product ·,· is defined by yHz=z,y=y¯z for all z,yCn, where yH=y¯ is the conjugate transpose of y. Denote by Cm×n the set of all m×n complex matrices. The transpose, conjugate and conjugate transpose of a matrix A=(aij)Cm×n are denoted by A=(aji), A¯=(aij¯) and AH=A¯, respectively. Set Q={(z,y)C2n:y=z¯}, where z¯ denotes the conjugate of zCn. Clearly, Q is a closed convex cone; see, e.g., [11]. A matrix A is called Hermitian iff AH=A; it is called positive semidefinite iff all of its eigenvalues are absolutely positive. For a complex number z=a+biC, we denote the real part and the imaginary part of z by Rez=a and Imz=b, respectively. For a polyhedral cone S={ξCp:Re(Kξ)0} with KCk×p, the dual cone of S is defined as S={μCp:Reξ,μ0,ξS}. Clearly, S=S. A convex subset DS is said to be a base of the polyhedral cone S if and only if 0clD and S=coneD:={s:s=λd,λ0,dD}, where clD 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:

minζRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]subjectto-h(ζ,ω)S,ζ=(z,z¯)C2n, UCFP

where A,BCn×n are positive semidefinite Hermitian matrices, SCp is a polyhedral cone which is specified by KCk×p, ηU, γV, ωW are uncertain parameters, the uncertain subsets U, V and W of C2m are nonempty and compact, f,g:C2n×C2mC and h:C2n×C2mCp are continuous with respect to the second argument, and f(·,η), g(·,γ) and h(·,ω) are analytic at each ζ=(z,z¯)C2n. We denote the feasible solutions set of (UCFP) by X(ω)={ζ=(z,z¯)C2n:-h(ζ,ω)S}.

If f and g are analytic with respect to the first argument, the problem (UCFP) is also nondifferentiable when either zHAz or zHBz vanishes at some point ζ0=(z0,z0¯) with z0HAz0=0 or z0HBz0=0, because the term (zHAz)12 or (zHBz)12 is nondifferentiable in the neighborhood of ζ0.

Throughout this paper, we assume that for each (ζ,η)X×U and (ζ,γ)X×V, Re[f(ζ,η)+(zHAz)12]0 and Re[g(ζ,γ)-(zHBz)12]>0. We adopt the robust optimization method to deal with (UCFP) in the worst-case scenarios. The robust counterpart of (UCFP) can be formulated as

minζmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]subjectto-h(ζ,ω)S,ωW,ζ=(z,z¯)C2n. RCFP

The feasible solution set of (RCFP) is denoted by

F:={ζ=(z,z¯)C2n:h(ζ,ω)-S,ωW},

which is called the robust feasible set of (UCFP). Since h is analytic with respect to the first argument, X(ω) is closed for each ωW and so, F=ωWX(ω) is closed. A point ζ0F is called a robust optimal solution of (UCFP) iff it is an optimal solution of (RCFP):

max(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]max(η,γ)U×VRe[f(ζ0,η)+(zHAz)12]Re[g(ζ0,γ)-(zHBz)12],ζF.

Observed that the problem (RCFP) is nondifferentiable if either zHAz or zHBz vanishes at some point ζ0=(z0,z0¯) with z0HAz0=0 or z0HBz0=0, since the term (zHAz)12 or (zHBz)12 is nondifferentiable in the neighborhood of ζ0.

We now recall some definitions and basic results which will be used in the sequel.

Definition 2.1

[27] Let ξ0S. The set S(ξ0) is defined to be the intersection of those closed half spaces which determines S and include ξ0 in their boundaries or, equivalently,

S(ξ0)={zCp:Re(K1ξ)0},

where K1Cd×p is an arbitrary submatrix of KCk×p and dk.

Clearly, SS(ξ0) for each ξ0S. In particular, S(ξ0)=Cp when ξ0intS. This implies that S(ξ0)S, where S(ξ0) is the dual cone of S(ξ0).

Lemma 2.1

[20] Let ηUC2m,ωWC2m and the mapping f(·,η):C2nC and h(·,ω):C2nCp be analytic at each ζ=(z,z¯)C2n. Then, for ζ0=(z0,z0¯)C2n,

f(ζ,η)-f(ζ0,η)=fζ(ζ0,η)(ζ-ζ0)+o(|ζ-ζ0|),ζC2n,h(ζ,ω)-h(ζ0,ω)=hζ(ζ0,ω)(ζ-ζ0)+o(|ζ-ζ0|),ζC2n,

where |ζ-ζ0| means the norm of complex vector ζ-ζ0,

fζ(ζ0,η)(ζ-ζ0)=zf(ζ0,η),z¯f(ζ0,η)z-z0z¯-z0¯=zf(ζ0,η)(z-z0)+z¯f(ζ0,η)(z¯-z0¯)C,

and

hζ(ζ0,ω)(ζ-ζ0)=zh(ζ0,ω),z¯h(ζ0,ω)z-z0z¯-z0¯=zh(ζ0,ω)(z-z0)+z¯h(ζ0,ω)(z¯-z0¯)Cp.

Lemma 2.2

Let f(·,η):C2nC and h(·,ω):C2nCp be analytic at ζ=(z,z¯)C2n, ACn×n be a positive semidefinite Hermitian matrix, and ηU and ωW be uncertain parameters. For each yCn, μCp, if the function

Φ(ζ)=f(ζ,η)+zHAy+h(ζ,ω),μ

is differentiable at ζ0=(z0,z0¯)C2n, then

Re[Φ(ζ0)(ζ-ζ0)]=Re[z-z0,zf(ζ0,η)¯+z¯f(ζ0,η)+Ay+μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)].

Proof

Noted that h(ζ,ω),μ=μHh(ζ,ω)=μ¯h(ζ,ω) and zHAy=z¯Ay. Since Φ is differentiable at ζ0, then

Φ(ζ0)=fζ(ζ0,η)+(zHAy)ζ+μHhζ(ζ0,ω)=(zf(ζ0,η),z¯f(ζ0,η))+(0,Ay)+μH(zh(ζ0,ω),z¯h(ζ0,ω)).

From Definition 2.1, it follows that

Φ(ζ0)(ζ-ζ0)=(zf(ζ0,η),z¯f(ζ0,η))z-z0z¯-z0¯+Ay(z¯-z0¯)+(zh(ζ0,ω),z¯h(ζ0,ω))z-z0z¯-z0¯,μ=(zf(ζ0,η)+μHzh(ζ0,ω))(z-z0)+(μHz¯h(ζ0,ω)+Ay+z¯f(ζ0,η))(z¯-z0¯)=z-z0,zf(ζ0,η)¯+μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)+Ay+z¯f(ζ0,η),z-z0.

For any x,yCn, x,y=y¯x,x,y¯=(y¯x)¯=yx¯=(yx¯)=x¯y=y,x and so,

Rex,y=Rey,x¯=Rey,x. 1

Therefore, one has

Re[Φ(ζ0)(ζ-ζ0)]=Re[z-z0,zf(ζ0,η)¯+μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)+Ay+z¯f(ζ0,η),z-z0]=Re[z-z0,zf(ζ0,η)¯+μzh(ζ0,ω)¯]+Re[μHz¯h(ζ0,ω)+Ay+z¯f(ζ0,η),z-z0]=Re[z-z0,zf(ζ0,η)¯+μzh(ζ0,ω)¯]+Re[z-z0,μHz¯h(ζ0,ω)+Ay+z¯f(ζ0,η)]=Re[z-z0,zf(ζ0,η)¯+z¯f(ζ0,η)+Ay+μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)],

where the third equality follows from (1), and Re(ν+υ)=Reν+Reυ implies the second and fourth equalities, ν,υC.

Remark 2.1

If h is uncertain-free with respect to the uncertain parameter ωW, that is, for any ω~,ω^W, h(ζ,ω~)=h(ζ,ω^), then Lemma 2.2 reduces to Lemma 2 of [17].

Lemma 2.3

[24] Let ECp×n, ACn×n, bCn and μS. Then the following assertions are equivalent:

  1. The system
    EHμ=Au+b,uHAu1,
    has a solution uCn.
  2. If EzS for zCn, then Re[(zHAz)12+bHz]0.

Lemma 2.4

[25] Let ξ0S and μ(S(ξ0)). Then Re(μHξ0)=0.

Lemma 2.5

[24] Let ACn×n and z,uCn. Then the following generalized Schwarz inequality in complex spaces holds:

Re(zHAu)(zHAz)12(uHAu)12,

and the equality holds whenever Az=λAu or z=λu for λ0.

In the rest of the paper, we assume that all complex functions f, g and h are defined on the linear manifold Q={ζ=(z,z¯)C2n:zCn} over the real field R, 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:

graphic file with name 10957_2021_1829_Equ98_HTML.gif

where the parameter v is defined as

v=max(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12].

Since Re[f(ζ,η)+(zHAz)12]0 and Re[g(ζ,γ)-(zHBz)12]>0 for each (ζ,η)X×U and (ζ,γ)X×V, one can conclude that v0. Since f(ζ,·) and g(ζ,·) are continuous, and U and V are compact sets, for each ζ=(z,z¯)F, there exist η~U and γ~V such that

Re[f(ζ,η~)+(zHAz)12]Re[g(ζ,γ~)-(zHBz)12]=max(η1,γ1)U×VRe[f(ζ,η1)+(zHAz)12]Re[g(ζ,γ1)-(zHBz)12].

For the sake of brevity, for each ζQ, we set

Y(ζ)=(η,γ)U×V:Re[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]=max(η1,γ1)U×VRe[f(ζ,η1)+(zHAz)12]Re[g(ζ,γ1)-(zHBz)12],

and

Yv(ζ)={(η,γ)U×V:Ref(ζ,η)+(zHAz)12-vReg(ζ,γ)-(zHBz)12=max(η1,γ1)U×V{Ref(ζ,η1)+(zHAz)12-vReg(ζ,γ1)-(zHBz)12}}.

It is easy to see that Y(ζ) and Yv(ζ) are compact subsets of U×V.

The next result shows the equivalence between the robust optimal solution of (UCFP) and ().

Theorem 3.1

  1. ζ0=(z0,z0¯)F is a robust optimal solution of (UCFP) with optimal value
    v=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12]=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12],
    if and only if
    minζFmax(η,γ)U×V{Re[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]}=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]-vRe[g(ζ0,γ)-(z0HBz0)12]=0,
    i.e., ζ0 is an optimal solution of () with v=v and optimal value 0.
  2. If ζ0 is a robust optimal solution of (UCFP) with optimal value v, then Y(ζ0)=Yv(ζ0).

Proof

(a) Let ζ0=(z0,z0¯) be a robust optimal solution of (UCFP) with optimal value

v=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12]=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12].

Then, one has

vRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12],ηU,γV,

which yields that Re[f(ζ0,η)+(z0HAz0)12]-vRe[g(ζ0,γ)-(z0HBz0)12]0 for all ηU, γV. Consequently, we have

minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]-vRe[g(ζ0,γ)-(z0HBz0)12]0.

Since f(·,η), g(·,γ) are analytic on the closed linear manifold Q for any ηU,γV, F is closed and FQ, we can assume that there exists ζ1=(z1,z1¯)F such that

max(η,γ)U×VRe[f(ζ1,η)+(z1HAz1)12]-vRe[g(ζ1,γ)-(z1HBz1)12]=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]0. 2

Let us show that max(η,γ)U×VRe[f(ζ1,η)+(z1HAz1)12]-vRe[g(ζ1,γ)-(z1HBz1)12]=0. Suppose by contradiction that

max(η,γ)U×VRe[f(ζ1,η)+(z1HAz1)12]-vRe[g(ζ1,γ)-(z1HBz1)12]<0.

So, Re[f(ζ1,η)+(z1HAz1)12]-vRe[g(ζ1,γ)-(z1HBz1)12]<0 for all ηU, γV. Then

max(η,γ)U×VRe[f(ζ1,η)+(z1HAz1)12]Re[g(ζ1,γ)-(z1HBz1)12]<v,

which contradicts the fact that v is the optimal value of (UCFP) at the robust optimal solution ζ0. This together with (2) implies that

0=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]-vRe[g(ζ0,γ)-(z0HBz0)12].

Therefore, ζ0 is an optimal solution of () with v=v and optimal value 0.

Conversely, assume that ζ0 is an optimal solution of () with v=v and optimal value 0. Then, one has

minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]-vRe[g(ζ0,γ)-(z0HBz0)12]=0. 3

Since f(ζ,·) and g(ζ,·) are continuous, U and V are compact sets, then there exist η1U and γ1V such that

Re[f(ζ0,η1)+(z0HAz0)12]-vRe[g(ζ0,γ1)-(z0HBz0)12]=0.

Since Re[g(ζ0,γ1)-(z0HBz0)12]>0, we have

v=Re[f(ζ0,η1)+(z1HAz1)12]Re[g(ζ0,γ1)-(z1HBz1)12]. 4

We next show that

v=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12]. 5

If (5) does not hold, then (4) implies

max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12]>v.

Since f(ζ,·) and g(ζ,·) are continuous, and U and V are compact sets, there exist η2U and γ2V such that

Re[f(ζ0,η2)+(z0HAz0)12]Re[g(ζ0,γ2)-(z0HBz0)12]>v,

and so, Re[f(ζ0,η2)+(z0HAz0)12]-vRe[g(ζ0,γ2)-(z0HBz0)12]>0, which contradicts (3). So, (5) holds.

We claim that

minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]=v.

If the above equality does not hold, then (5) implies that

minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]<v.

Since f(·,η) and g(·,γ) are analytic on the closed linear manifold Q for any ηU,γV, and FQ is closed, we can assume that there exists ζ2F such that

max(η,γ)U×VRe[f(ζ2,η)+(z2HAz2)12]Re[g(ζ2,γ)-(z2HBz2)12]=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]<v.

This implies that

Re[f(ζ2,η)+(z2HAz2)12]Re[g(ζ2,γ)-(z2HBz2)12]<v,(η,γ)U×V.

So, Re[f(ζ2,η)+(z2HAz2)12]-vRe[g(ζ2,γ)-(z2HBz2)12]<0 for all (η,γ)U×V. This together with the continuity of f and g and compactness of U and V yields that

max(η,γ)U×VRe[f(ζ2,η)+(z2HAz2)12]-vRe[g(ζ2,γ)-(z2HBz2)12]<0,

which contradicts that

minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]=0.

Consequently, we have

v=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]=max(η,γ)U×VRe[f(ζ0,η)+(z0HAz0)12]Re[g(ζ0,γ)-(z0HBz0)12].

So, ζ0=(z0,z0¯)F is the robust optimal solution of (UCFP) with optimal value v.

(b) It directly follows from (a) that Y(ζ0)=Yv(ζ0).

Remark 3.1

If f and g are perturbed by the same uncertain parameter, h is uncertain-free and U=V, then Theorem 4.5 reduces Theorem 1 in [16, p. 233]. Moreover, if A=B=0, U=V and h is uncertain-free, then Theorem 4.5 reduces Lemma 2.2 in [21, p. 177]. In particular, if fg and h are uncertain-free, f and g are also continuous and real-valued functions, A=B=0 and F is a compact and connected subset of R2n, 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 ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v, z0HAz0>0 and z0HBz0>0. Then there exist (α^,l^)R+2\{0}, μ^S, u^1,u^2Cn and ω^W such that

α^[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+(l^μ^)zh(ζ0,ω^)¯+(l^μ^)Hz¯h(ζ0,ω^)=0, 6
Reh(ζ0,ω^),l^μ^=0,u^1HAu^1=1,u^2HBu^2=1, 7
Re(z0HAz0)12=Re(z0HAu^1),Re(z0HBz0)12=Re(z0HBu^2). 8

Proof

It follows from Theorem 3.1 (a) that ζ0=(z0,z0¯)F is an optimal solution of () with v=v and optimal value 0. For each ηU, γV and ωW, f(·,η), g(·,γ) and h(·,ω) are analytic at each ζ=(z,z¯)Q, and AB are positive semidefinite Hermitian matrices; we deduce that for each μS, Re[f(·,η)+(zHAz)12]-vRe[g(·,γ)-(zHBz)12] and Reh(·,ω),μ are analytic at ζ0. We observe that

ζFmax(ω,μ)W×SReh(ζ,ω),μ0max(ω,μ)W×S1Reh(ζ,ω),μ0,

where S1={μS:|μ|1}. So, () with v=v is equivalent to the following optimization problem:

minζmax(η,γ)U×VRe{[f(ζ,η)+(zHAz)12]-v[g(ζ,γ)-(zHBz)12]},subjectto,ψ(ζ)0,ζ=(z,z¯)C2n, 9

where ψ(ζ)=max(ω,μ)W×S1Reh(ζ,ω),μ. Since h is analytic on Q with respect to the first argument, and W and S1 are nonempty compact sets, for each ζQ, there exists (ω,μ)W×S1 such that ψ(ζ)=Reh(ζ,ω),μ, ψ is differentiable at ζQ and so,

ψ(ζ)=μzh(ζ,ω)¯+μHz¯h(ζ,ω). 10

We define the generalized Lagrangian function of the problem (9) as follows:

L(ζ,α,l)=αmax(η,γ)U×V{Re[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]}+lψ(ζ),

where ζ=(z,z¯)C2n,α0 and l0. Since U and V are compact, for each ζF, there exist ηU and γV such that

L(ζ,α,l)=αRe[f(ζ,η)+(zHAz)12]-vRe[g(ζ,γ)-(zHBz)12]+lψ(ζ).

Since ζ0=(z0,z0¯)F is an optimal solution of () with v=v, ζ0 is also an optimal solution of (9). This together with (10) yields that there exist (α^,l^)R+2\{0}, η^U, γ^V, ω^W and μ^S1S such that

α^{[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Az0Az0,z012+vBz0Bz0,z012}+(l^μ^)zh(ζ0,ω^)¯+(l^μ^)Hz¯h(ζ0,ω^)=α^{[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Az0Az0,z012+vBz0Bz0,z012}+l^{μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)}=α^{[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Az0Az0,z012+vBz0Bz0,z012}+l^ψ(ζ0)=0

and Reh(ζ0,ω^),l^μ^=l^Reh(ζ0,ω^),μ^=l^ψ(ζ0)=0. Since z0HAz0=Az0,z0>0 and z0HBz0=Bz0,z0>0, we set u^1=z0Az0,z012 and u^2=z0Bz0,z012. Then, it shows that

α^{[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2}+(l^μ^)zh(ζ0,ω^)¯+(l^μ^)Hz¯h(ζ0,ω^)=0.

Moreover, we have

u^1HAu^1=z0HAz0(z0HAz0)12(z0HAz0)12=1,u^2HBu^2=z0HBz0(z0HBz0)12(z0HBz0)12=1,Re(z0HAu^1)=Re(z0HAz0Az0,z012)=Re(z0HAz0(z0HAz0)12)=Re(z0HAz0)12,

and Re(z0HBu^2)=Re(z0HBz0Bz0,z012)=Re(z0HBz0(z0HBz0)12)=Re(z0HBz0)12, as required.

Remark 4.1

  1. In Theorem 4.1, μ^S and l^0 imply l^μ^S since S is a closed and convex cone. So, Theorem 4.1 implies that there exist (α^,l^)R+2\{0}, μ~=l^μ^S, u^1,u^2Cn and ω^W such that
    α^[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ~zh(ζ0,ω^)¯+μ~z¯h(ζ0,ω^)=0, 11
    Reh(ζ0,ω^),μ~=0,u^1HAu^1=1,u^2HBu^2=1, 12
    Re(z0HAz0)12=Re(z0HAu^1),Re(z0HBz0)12=Re(z0HBu^2). 13
  2. Since 0S1S, for each ζF, (ω,0)W×S1 is a trivial solution of the problem: ψ(ζ):=max(ω,μ)W×S1Reh(ζ,ω),μ=0, that is, W×{0}ψζ-1(0), where
    ψζ-1(0)={(ω¯,μ¯)W×S1:Reh(ζ,ω¯),μ¯=max(ω,μ)W×S1Reh(ζ,ω),μ}.
    If ζF, then there exist μS,ωW such that Reh(ζ,ω),μ>0 due to S=(S). This yields that ψ(ζ)=max(ω,μ)W×S1Reh(ζ,ω),μ>0. In Theorem 4.1, if μ^=0 and α^=0, then one can easily check that (6)–(8) still hold for all l^>0. In this case, for any l^>0, (α^,μ~)=0, α^ and μ~ also satisfy (11)–(13), where μ~=l^μ^. In order to avoid the trivial case (α^,μ~)=0, let us analyze the set ψζ-1(0).
    • (i)
      If ψζ-1(0)=W×{0} for each ζF, then for each ζ=(z,z¯)C2n,
      maxωWReh(ζ,ω),μ<0,μS1\{0}.
      It implies F=Q, i.e., (UCFP), () with v=v and the optimization problem (9) are unconstrained. Then, there exist α^=1, μ^=0 and u^1,u^2Cn such that for each l^0,ω^W, μ~=l^μ^=0S, and the robust necessary optimality conditions (11)–(13) hold.
    • (ii)
      If there exists ζF such that ψζ-1(0)W×{0}, then, there exists μ^S1\{0} such that maxωWReh(ζ,ω),μ^=0. In particular, if all conditions of Theorem 4.1 hold and ψζ0-1(0)W×{0}, then there exist ω^W and a nonzero μ^S1S such that for (α^,l^)R+2\{0} given in Theorem 4.1, (α^,μ~)(R+×S)\{0}, where μ~=l^μ^.

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 ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v, ψζ0-1(0)W×{0}, z0HAz0>0 and z0HBz0>0. Then there exist ω^W, u^1,u^2Cn and (α^,μ^)R+×S\{0} such that

α^[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0, 14
Reh(ζ0,ω^),μ^=0,u^1HAu^1=1,u^2HBu^2=1, 15
Re(z0HAz0)12=Re(z0HAu^1),Re(z0HBz0)12=Re(z0HBu^2). 16

We next present another Fritz John-type robust necessary optimality conditions for the robust optimal solution of (UCFP) when S has a compact base.

Theorem 4.3

Let ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v, S have a compact base, and let z0HAz0>0 and z0HBz0>0. Then there exist ω^W, u^1,u^2Cn and (α^,μ^)R+×S\{0} such that (14)–(16) hold.

Proof

Since S has a compact base, we assume that D is a compact base of S. Then

ζFmax(ω,μ)W×SReh(ζ,ω),μ0max(ω,μ)W×DReh(ζ,ω),μ0.

Consequently, () with v=v is equivalent to the following optimization problem:

minζmax(η,γ)U×VRe{[f(ζ,η)+(zHAz)12]-v[g(ζ,γ)-(zHBz)12]},subjecttoφ(ζ)0,ζ=(z,z¯)C2n, 17

where φ(ζ)=max(ω,μ)W×DReh(ζ,ω),μ. Since fg and h are analytic on Q with respect to the first argument, and UVW and D are nonempty compact sets, for each ζQ, there exist η1U,γ1V and (ω,μ)W×D such that

Re{[f(ζ,η1)+(zHAz)12]-v[g(ζ,γ1)-(zHBz)12]}=max(η,γ)U×VRe{[f(ζ,η)+(zHAz)12]-v[g(ζ,γ)-(zHBz)12]},

φ(ζ)=Reh(ζ,ω),μ and so, max(η,γ)U×VRe{[f(ζ,η)+(zHAz)12]-v[g(ζ,γ)-(zHBz)12]} and φ(ζ) are differentiable at ζQ. Moreover, one has

φ(ζ)=μzh(ζ,ω)¯+μHz¯h(ζ,ω). 18

Since ζ0=(z0,z0¯)F is an optimal solution of () with v=v, ζ0 is also an optimal solution of (17). This together with (18) yields that there exist (α^,l^)R+2\{0}, η^U, γ^V, ω^W and μ~DS such that

α^{[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Az0Az0,z012+vBz0Bz0,z012}+(l^μ~)zh(ζ0,ω^)¯+(l^μ~)Hz¯h(ζ0,ω^)=0

and Reh(ζ0,ω^),l^μ~=l^Reh(ζ0,ω^),μ~=l^φ(ζ0)=0. Since μ~D and D is a compact base of S, then μ~0. Using (α^,l^)R+2\{0} yields (α^,l^μ~)(R+×S)\{0}. Set μ^:=l^μ~, u^1=z0Az0,z012 and u^2=z0Bz0,z012. 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 ζ0=(z0,z0¯) if and only if for any nonzero μSCp and for any ωW,

Rehζ(ζ0,ω)(ζ-ζ0),μ0,ζζ0.

It is easy to verify that if (UCFP) satisfies RCQ at ζ0, then

μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)0,μS\{0},ωW,

and for any ωW, μzh(ζ0,ω)¯+μHz¯h(ζ0,ω)=0 implies μ=0.

If Re[g(ζ,γ)-(zHBz)12]1 and h is uncertain-free with respect to the uncertain parameter ωW, 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 S=R+p and h:RnRp is differentiable and uncertain-free, then RCQ reduces to the following positively linearly independent constraint qualification (in short, PLICQ): For a feasible point z0Rn with h(z0)=0, there exists no nonzero μ=(μ1,μ2,,μp)R+p such that i=1pμihi(z0)=0.

For a feasible point z0Rn, RCQ is slightly weaker than the following quasinormality condition: There exist no nonzero μ=(μ1,μ2,,μp)R+p and no sequence {zk}z0 such that i=1pμihi(z0)=0 and for all k, μihi(zk)>0 for all i with μi0.

We present the Karush–Kuhn–Tucker-type robust necessary optimality conditions for (UCFP) by using RCQ.

Theorem 4.4

Let ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v. Assume that (UCFP) satisfies RCQ at ζ0 with z0HAz0>0 and z0HBz0>0. If ψζ0-1(0)W×{0} or S has a compact base, then there exist μ^S, u^1,u^2Cn and ω^W such that

[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0, 19
Reh(ζ0,ω^),μ^=0, 20
u^1HAu^1=1,u^2HBu^2=1, 21
Re(z0HAz0)12=Re(z0HAu^1),Re(z0HBz0)12=Re(z0HBu^2). 22

Proof

It follows from Theorems 4.2 and 4.3 that there exist (α^,μ~)R+×S\{0}, u^1,u^2Cn and ω^W such that (21), (22) hold and

α^[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ~zh(ζ0,ω^)¯+μ~Hz¯h(ζ0,ω^)=0, 23

and Reh(ζ0,ω^),μ~=0.

If α^=0 in (23), then

μ~zh(ζ0,ω^)¯+μ~Hz¯h(ζ0,ω^)=0. 24

Since (UCFP) satisfies RCQ at ζ0, then (24) implies μ~=0, which contradicts the fact that (α^,μ~)R+×S\{0}. So, α^>0. Divided both the sides of equation (23) by α^ and set μ^=μ~α^S, we have

[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0,

and Reh(ζ0,ω^),μ^=1α^Reh(ζ0,ω^),μ~=0. 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 ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v. Assume that (UCFP) satisfies RCQ at ζ0 with z0HAz0>0 and z0HBz0>0. If ψζ0-1(0)W×{0} or S has a compact base, then there exist μ^S, u^1,u^2Cn, ω^W and a positive integer k such that

  • (i)

    (ηi^,γi^)Y(ζ0), i=1,2,,k;

  • (ii)
    there exist multipliers λ^i>0 for i=1,2,,k with i=1kλ^i=1 such that
    i=1kλ^i[zf(ζ0,ηi^)¯+z¯f(ζ0,ηi^)]-v[zg(ζ0,γi^)¯+z¯g(ζ0,γi^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0, 25
    Reh(ζ0,ω^),μ^=0, 26
    u^1HAu^1=1,Re(z0HAz0)12=Re(z0HAu^1), 27
    u^2HBu^2=1,Re(z0HBz0)12=Re(z0HBu^2). 28

Proof

It follows from Theorem 3.1 that ζ0=(z0,z0¯)F is an optimal solution of () with v=v and optimal value 0, and Yv(ζ0)=Y(ζ0). For each ηU, γV and ωW, f(·,η), g(·,γ) and h(·,ω) are analytic at each ζ=(z,z¯)Q, and AB are positive definite Hermitian matrices; we deduce that for a nonzero vector μS, Reh(·,ω),μ and Re[f(·,η)+(zHAz)12]-vRe[g(·,γ)-(zHBz)12] are analytic at ζ. 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 ζ0=(z0,z0¯)F, there exist (α^,μ~)R+×S\{0}, ω^W, u^1,u^2Cn and a positive integer k with (η^i,γ^i)Yv(ζ0),λ^i>0 and i=1kλ^i=1, i=1,2,,k such that

α^i=1kλ^i{[zf(ζ0,η^i)¯+z¯f(ζ0,η^i)]-v[zg(ζ0,γ^i)¯+z¯g(ζ0,γ^i)]+Au^1+vBu^2}+μ~zh(ζ0,ω^)¯+μ~Hz¯h(ζ0,ω^)=0,

Reh(ζ0,ω^),μ~=0 and (27) and (28) hold.

Since Yv(ζ0)=Y(ζ0), we have (η^i,γ^i)Y(ζ0),i=1,2,,k. Since (UCFP) satisfies RCQ at ζ0, as in the proof of Theorem 4.4, we have α^>0. Set μ^=μ~α^S. Then

i=1kλ^i{[zf(ζ0,η^i)¯+z¯f(ζ0,η^i)]-v[zg(ζ0,γ^i)¯+z¯g(ζ0,γ^i)]}+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0

and Reh(ζ0,ω^),μ^=1α^Reh(ζ0,ω^),μ~=0, namely, (25) and (26) are true.

Remark 4.2

Generally, the multipliers μS 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 μ^S. 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, A=B=0 and U=V, and Theorem 2 in [16, p. 234] even though fg are perturbed by the same uncertain parameter, h is uncertain-free and U=V. Moreover, if h is uncertain-free with respect to the uncertain parameter ωW and Re[g(ζ,γ)-(zHBz)12]1, 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 ζ0 of (UCFP) when Az0,z0=0 or Bz0,z0=0, i.e., Az,z12 or Bz,z12 is nondifferentiable at z=z0. By the compactness of Y(ζ0), one can verify that Theorem 4.5 (a) and (b) are satisfied. Let (ηi^,γi^)Y(ζ0),λ^i>0, i=1,2,k be the same as in Theorem 4.5. Since W is a nonempty and compact set, there exists ω^W such that

maxμS1Reh(ζ0,ω^),μ=maxωWmaxμS1Reh(ζ0,ω),μ=max(ω,μ)W×S1Reh(ζ0,ω),μ.

Set v=minζFmax(η,γ)U×VRe[f(ζ,η)+(zHAz)12]Re[g(ζ,γ)-(zHBz)12]. Motivated by the works [16, 17, 27], we introduce a subset Z(ζ0)C2n as follows:

Z(ζ0)={ζQ:-hζ(ζ0,ω^)ζS(-h(ζ0,ω^)),ifanyoneof (c1), (c2)and (c3)holds},

where the conditions (c1), (c2) and (c3) are defined as:

  1. Re{i=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)]ζ+Az0,zAz0,z012+(v)2Bz,z12}<0, if z0HAz0>0 and z0HBz0=0;

  2. Re{i=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)]ζ+Az,z12+vBz0,zBz0,z012}<0, if z0HAz0=0 and z0HBz0>0;

  3. Re{i=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)]ζ+[A+(v)2B]z,z12}<0, if z0HAz0=0 and z0HBz0=0.

We next present Karush–Kuhn–Tucker-type robust necessary optimality conditions for (UCFP) without the assumptions Az0,z0>0, Bz0,z0>0 as well as the assumptions that ψζ0-1(0)W×{0} and S has a compact base.

Theorem 4.6

Let ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v. Assume that (UCFP) satisfies RCQ at ζ0 and Z(ζ0)=. Then there exist μ^S, u^1,u^2Cp such that

i=1kλ^i[zf(ζ0,ηi^)¯+z¯f(ζ0,ηi^)]-v[zg(ζ0,γi^)¯+z¯g(ζ0,γi^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0, 29
Reh(ζ0,ω^),μ^=0, 30
u^1HAu^11,Re(z0HAz0)12=Re(z0HAu^1), 31
u^2HBu^21,Re(z0HBz0)12=Re(z0HBu^2). 32

Proof

Observe that Az,z=zHAz=ζHA^ζ=A^ζ,ζ, Bz,z=zHBz=ζHB^ζ=B^ζ,ζ, where ζ=(z,z¯)C2n and

A^=A000C2n×2n,B^=B000C2n×2n.

We split the proof into three cases.

(1) If z0HAz0>0 and z0HBz0=0, then we deduce from Z(ζ0)= that for ζ=(z,z¯)C2n and -hζ(ζ0,ω^)ζS(-h(ζ0,ω^)),

Rei=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)]ζ+Az0,zAz0,z012+(v)2Bz,z120.

It can be equivalently reformulated as

Rei=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)+A^ζ0Az0,z012]ζ+(v)2B^ζ,ζ120. 33

Note that -h(ζ0,ω^)S. By Lemma 2.4, it implies that there exists μ^S(-h(ζ0,ω^)) such that -Reh(ζ0,ω^),μ^=Re(μ^H(-h(ζ0,ω^)))=0 and so, Reh(ζ0,ω^),μ^=0. Hence, we conclude from S(-h(ζ0,ω^))S that μ^S and

max(ω,μ)W×SReh(ζ0,ω),μ=max(ω,μ)W×S1Reh(ζ0,ω),μ=Reh(ζ0,ω^),μ^=0.

Set E=-hζ(ζ0,ω^), b=i=1kλ^i[fζ(ζ0,ηi^)-vgζ(ζ0,γi^)+A^ζ0Az0,z012], z=ζ, A=(v)2B^ in Lemma 2.3. It follows from Lemma 2.3(a) that there exists ξ=(α,α¯)C2n such that αH(v)2Bα=ξH(v)2B^ξ1, and

(-h(ζ0,ω^))Hμ^=(v)2B^ξ+i=1kλ^ifζ(ζ0,ηi^)-vgζ(ζ0,γi^)+A^ζ0Az0,z012. 34

Set u^2=vα. Then (34) implies that

-zh(ζ0,ω^)¯μ^-z¯h(ζ0,ω^)¯μ^=vBu^2+i=1kλ^izf(ζ0,ηi^)¯-vzg(ζ0,γi^)¯+(Az0)HAz0,z012i=1kλ^iz¯f(ζ0,ηi^)¯-vz¯g(ζ0,γi^)¯,

and so,

vBu^2+i=1kλ^izf(ζ0,ηi^)¯-vzg(ζ0,γi^)¯+(Az0)HAz0,z012+zh(ζ0,ω^)¯μ^=0, 35
i=1kλ^iz¯f(ζ0,ηi^)¯-vz¯g(ζ0,γi^)¯+z¯h(ζ0,ω^)¯μ^=0. 36

Clearly, (36) can be equivalently expressed as

i=1kλ^iz¯f(ζ0,ηi^)-vz¯g(ζ0,γi^)+z¯h(ζ0,ω^)μ^¯=0. 37

Since ACn×n is positive semidefinite Hermitian matrix, we have (Az0)H=z0HA. Due to z0HAz0>0, set u^1=z0Az0,z012. So, (29) is obtained by summing (35) and (37). By the same proof as in (27), (31) can be obtained.

Owing to u^2=vα and αH(v)2Bα1, we obtain u^2HBu^21. It follows from Lemma 2.5 and z0HBz0=0 that for uCn, Re(z0HBu)=Re(uHBz0)(z0HBz0)12(uHBu)12=0. Taking u=Bz0 in the above equality, one has Re[(Bz0)HBz0]=ReBz0,Bz00 and so, Bz0=0. Since B is positive semidefinite Hermitian matrix, then z0HB=(Bz0)H=0. It therefore shows that Re(z0HBz0)12=Re(z0HBu^2)=0.

(2) If z0HAz0=0 and z0HBz0>0, 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 z0HAz0=0 and z0HBz0=0, 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 μS 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 μ^S. 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 U=V, and Theorem 6 in [17, p. 1207] when h is uncertain-free with respect to the uncertain parameter ωW and Re[g(ζ,γ)-(zHBz)12]1.

In particular, if we set k=1 in Theorem 4.6, then the following robust necessary optimality conditions for (UCFP) holds.

Corollary 4.1

Let ζ0=(z0,z0¯)F be a robust optimal solution of (UCFP) with optimal value v. Assume that (UCFP) satisfies RCQ at ζ0 and Z(ζ0)=. Then there exist μ^S, u^1,u^2Cp, η^U, γ^V and ω^W such that

[zf(ζ0,η^)¯+z¯f(ζ0,η^)]-v[zg(ζ0,γ^)¯+z¯g(ζ0,γ^)]+Au^1+vBu^2+μ^zh(ζ0,ω^)¯+μ^Hz¯h(ζ0,ω^)=0,Reh(ζ0,ω^),μ^=0,u^1HAu^11,u^2HBu^21,Re(z0HAz0)12=Re(z0HAu^1),Re(z0HBz0)12=Re(z0HBu^2).

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 ζ0 of (UCFP) are established under the assumption that S has a compact base or, ψζ0-1(0)W×{0}. By the RCQ, Karush–Kuhn–Tucker-type robust necessary optimality conditions for the robust optimal solution ζ0 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

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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.

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