Robust Optimization over Multiple Domains

Robust Optimal Desirability Approach for Multiple Responses Optimization with Multiple Productions Scenarios

  An optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. In dynamic environments, changes in production requirements in each condition create different production scenarios. Therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted r...

Towards 5G Network Slicing over Multiple-Domains

Robust Portfolio Optimization with Multiple Experts

The success of quantitative approaches to portfolio choice crucially depends on the considered return model. Experts however do not agree on which return model is most appropriate. This controversy about the model specification introduces uncertainty in the optimal portfolio choice. We will not meddle in the discussion on which model specification is most appropriate. Instead we consider the ad...

Guaranteed Non-convex Optimization: Submodular Maximization over Continuous Domains

Submodular continuous functions are a category of (generally) non-convex/nonconcave functions with a wide spectrum of applications. We characterize these functions and demonstrate that they can be maximized efficiently with approximation guarantees. Specifically, I) for monotone submodular continuous functions with an additional diminishing returns property, we propose a Frank-Wolfe style algor...

robust optimal desirability approach for multiple responses optimization with multiple productions scenarios

an optimal desirability function method is proposed to optimize multiple responses in multiple production scenarios, simultaneously. in dynamic environments, changes in production requirements in each condition create different production scenarios. therefore, in multiple production scenarios like producing in several production lines with different technologies in a factory, various fitted res...