1 Introduction
Convexity and generalized convexity play a significant role in many fields, for example, in biological system, economy, optimization, and so on [
1‐
5].
Generalized convex functions, labeled as semilocal convex functions, were introduced by Ewing [
6] by using more general semilocal preinvexity and
η-semidifferentiability. After that optimality conditions for weak vector minima were given [
7]. Also, optimality conditions and duality results for a nonlinear fractional involving
η-semidifferentiability were established [
8].
Furthermore, some optimality conditions and duality results for semilocal E-convex programming were established [
9]. E-convexity was extended to E-preinvexity [
10]. Recently, semilocal E-preinvexity (SLEP) and some of its applications were introduced [
11‐
13].
Generalized convex functions in manifolds, such as Riemannian manifolds, were studied by many authors; see [
14‐
17]. Udrist [
18] and Rapcsak [
19] considered a generalization of convexity called geodesic convexity. In this setting, the linear space is replaced by a Riemannian manifold and the line segment by a geodesic one. In 2012, geodesic E-convex (GEC) sets and geodesic E-convex (GEC) functions on Riemannian manifolds were studied [
20]. Moreover, geodesic semi E-convex (GsEC) functions were introduced [
21]. Recently, geodesic strongly E-convex (GSEC) functions were introduced and some of their properties were discussed [
22].
2 Geodesic semilocal E-preinvexity
Assume that ℵ is a complete n-dimensional Riemannian manifold with Riemannian connection ▽. Let \(\kappa _{1}, \kappa _{2} \in \aleph \) and \(\gamma \colon [0,1]\longrightarrow \aleph \) be a geodesic joining the points \(\kappa _{1} \) and \(\kappa _{2} \), which means that \(\gamma _{\kappa _{1},\kappa _{2}}(0)= \kappa _{2}\) and \(\gamma _{\kappa _{1},\kappa _{2}}(1)=\kappa _{1} \).
Hence, \(h(m) \) is not a GSEP function on \(\mathbb{R} \) with respect to η.
The above lemma is proved directly by using definitions (Definition
2.4, Definition
2.5, Definition
2.6, and Definition
2.4).
3 Optimality criteria
In this section, let us consider the nonlinear fractional multiobjective programming problem
$$\begin{aligned} (\mbox{VFP}) \textstyle\begin{cases} \mbox{minimize } \frac{f(\kappa )}{g(\kappa )}= (\frac{f_{1}(\kappa )}{g_{1}( \kappa )},\ldots ,\frac{f_{p}(\kappa )}{g_{p}(\kappa )} ), \\ \mbox{subject to } h_{j}(\kappa )\leq 0,\quad j\in Q={1,2,\ldots, q} \\ \kappa \in K_{0}; \end{cases}\displaystyle \end{aligned}$$
where
\(K_{0}\subset \aleph \) is a GLEI set and
\(g_{i}(\kappa )>0\),
\(\forall \kappa \in K_{0} \),
\(i\in P={1,2,\ldots , p} \).
Let \(f=(f_{1},f_{2},\ldots , f_{p})\), \(g=(g_{1},g_{2},\ldots ,g_{p}) \), and \(h=(h_{1},h_{2},\ldots ,h_{q}) \)
and denote that \(K= \lbrace \kappa :h_{j}(\kappa )\leq 0, j \in Q, \kappa \in K_{0} \rbrace \), the feasible set of problem (VFP).
For
\(\kappa ^{*}\in K \), we put
$$ Q\bigl(\kappa ^{*}\bigr)= \bigl\lbrace j:h_{j}\bigl(\kappa ^{*}\bigr)= 0, j\in Q \bigr\rbrace , \qquad L\bigl(\kappa ^{*}\bigr)=\frac{Q}{Q(\kappa ^{*})}. $$
We also formulate the nonlinear multiobjective programming problem as follows:
$$\begin{aligned} (\mbox{VFP}_{\lambda }) \textstyle\begin{cases} \mbox{minimize } ( f_{1}(\kappa )-\lambda _{1}g_{1}(\kappa ),\ldots f_{p}(\kappa )-\lambda _{p}g_{p}(\kappa ) ), \\ \mbox{subject to } h_{j}(\kappa )\leq 0,\quad j\in Q={1,2,\ldots, q} \\ \kappa \in K_{0}; \end{cases}\displaystyle \end{aligned}$$
where
\(\lambda =(\lambda _{1},\lambda _{2},\ldots ,\lambda _{p})\in \mathbb{R}^{p} \).
The following lemma connects the weak efficient solutions for (VFP) and (\(\mathrm{VFP}_{\lambda } \)).
Next, some sufficient optimality conditions for the problem (VFP) are established.
Similarly we can prove the next theorem.
The dual problem for (VFP) is formulated as follows:
$$\begin{aligned} (\mathrm{VFD}) \textstyle\begin{cases} \mbox{minimize } (\zeta _{i}, i=1,2,\ldots , p ) , \\ \mbox{subject to } \sum_{i=1}^{p}\alpha _{i} (f'_{i+} ( \gamma _{\lambda ,E( \kappa )}(t) ) -\zeta _{i}g'_{i+} ( \gamma _{\lambda ,E( \kappa )}(t) ) ) +\sum_{j=1}^{m}\beta _{j}h'_{j+} ( \gamma _{\lambda ,E(\kappa )}(t) ) \geqslant 0 \\ \quad \kappa \in K_{0}, t\in [0,1], \\ \quad f_{i}(\lambda )-\zeta _{i}g_{i}(\lambda )\geqslant 0, \quad i\in P,\qquad \beta _{j}h_{j}(\lambda )\geqslant 0, \quad j\in \aleph ; \end{cases}\displaystyle \end{aligned}$$
where
\(\zeta =(\zeta _{i}, i=1,2,\ldots , p)\geqslant 0\),
\(\alpha =( \alpha _{i}, i=1,2,\ldots , p)> 0\),
\(\beta =(\beta _{i}, i=1,2,\ldots , m)\geqslant 0\),
\(\lambda \in K_{0}\).
Denote the feasible set problem (\(VFD \)) by \(K^{\prime} \).
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