Role of \((\rho , \eta , A)\)-invexity to \(\varepsilon \)-optimality conditions for multiple objective fractional programming
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Publication:440729
DOI10.1016/j.amc.2012.01.068zbMath1245.90119OpenAlexW2078705183MaRDI QIDQ440729
Publication date: 19 August 2012
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2012.01.068
\(\varepsilon \)-efficient solutiongeneralized non-convexitygeneralized nonconvexitymultiple objective fractional subset programmingsemi-parametric sufficient \(\varepsilon \)-optimality conditions
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Cites Work
- General parametric sufficient optimality conditions for multiple objective fractional subset programming relating to generalized \((\rho,\eta,A)\) -invexity
- Generalized convexity and vector optimization.
- Optimization theory for n-set functions
- Generalized \((\rho ,\theta )\)-\(\eta \) invariant monotonicity and generalized \((\rho ,\theta )\)-\(\eta \) invexity of nondifferentiable functions
- On sufficiency of the Kuhn-Tucker conditions
- On the optimality conditions of vector-valued \(n\)-set functions
- Efficiency conditions and duality models for multiobjective fractional subset programming problems with generalized \(({\mathcal F},\alpha,\rho,\theta)\)-\(V\)-convex functions
- On \(\varepsilon\)-optimality conditions for multiobjective fractional optimization problems
- Optimality principles and duality models for a class of continuous-time generalized fractional programming problems with operator constraints
- New Sequential Lagrange Multiplier Conditions Characterizing Optimality without Constraint Qualification for Convex Programs
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