Contributed Session Wed.2.H 1029

Wednesday, 13:15 - 14:45 h, Room: H 1029

Cluster 15: Multi-objective optimization [...]

Nonlinear multiobjective optimization

 

Chair: Ingrida Steponavice

 

 

Wednesday, 13:45 - 14:10 h, Room: H 1029, Talk 2

Ingrida Steponavice
On robustness for simulation-based multiobjective optimization

Coauthor: Kaisa Miettinen

 

Abstract:
Many real-world engineering design problems are too complex to be modeled analytically and involve the use of computer simulations. In simulation-based applications, performance of a system is evaluated based on the output from a simulation model which is typically subject to various sources of uncertainty. In design optimization, the designer or the decision maker may prefer a robust solution which is as “good” as possible and at the same time leads to small performance variations that appear due to uncertainty. Robustness in this context is understood as an insensitivity of objective functions values to some uncertainty arising due to stochastic processes inside the simulation model. We survey the approaches for robust simulation-based multiobjective optimization proposed in the literature and discuss the multiobjective robustness measures that can be used to find robust solutions.

 

 

Wednesday, 14:15 - 14:40 h, Room: H 1029, Talk 3

Luis Roman Lucambio Perez
A modified subgradient algorithm for solving K-convex inequalities

Coauthor: Jose Yunier Bello Cruz

 

Abstract:
Thirty years ago, Robinson proposed a subgradient method for solving K-convex inequalities in finite dimensional spaces. In this work, we propose a modification of this method that allows to solve systems of K-convex inequalities in Hilbert spaces, and has two advantages: first, without additional hypotheses, it was possible to show that it converges strongly to a solution of the problem, and second, it has the desirable property that the limit point is the closest solution to the starting point. To prove that our algorithm is well defined it was necessary to show that the set of sub-gradients is non-empty at interior points of the domain. We demonstrate this fact when the cone K is finitely generated. To our knowledge, this is the first time it is proved the existence of such sub-differentials of vectorial K-convex functions in infinite dimensional spaces.

 

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