Open AccessCharacterization of Robust Solution Quadratically Constrained Quadratic Optimization Problem Subjected to Data Uncertainty
Author(s) -
Moussa Barro,
Satafa Sanogo,
Mohamed Zongo,
Sado Traoré
Publication year - 2021
Language(s) - English
DOI - 10.47260/jamb/1111
Subject(s) - quadratic growth , robust optimization , quadratically constrained quadratic program , optimization problem , mathematical optimization , quadratic programming , duality (order theory) , convex optimization , mathematics , quadratic equation , quadratic function , constrained optimization , constraint (computer aided design) , computer science , regular polygon , algorithm , geometry , discrete mathematics
Robust Optimization (RO) arises in two stages of optimization, first level for maximizing over the uncertain data and second level for minimizing over the feasible set. It is the most suitable mathematical optimization procedure to solve real-life problem models. In the present work, we characterize robust solutions for both homogeneous and non-homogeneous quadratically constrained quadratic optimization problem where constraint function and cost function are uncertain. Moreover, we discuss about optimistic dual and strong robust duality of the considered uncertain quadratic optimization problem. Finally, we complete this work with an example to illustrate our solution method.Mathematics Subject Classification: (2010) 90C20 - 90C26 - 90C46-90C47Keywords: Robust Optimization, Data Uncertainty, Quadratic Optimization Strong Duality, Robust Solution, DPJ-Convex.