Now showing items 1-5 of 5
A randomized method for handling a difficult function in a convex optimization problem, motivated by probabilistic programming
We propose a randomized gradient method for the handling of a convex function whose gradient computation is demanding. The method bears a resemblance to the stochastic approximation family. But in contrast to stochastic ...
Learning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization
Several emerging applications, such as “Analytics of Things" and “Integrative Analytics" call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses ...
Scenariao Reduction Revisited: Fundamental Limits and Gurarantees
The goal of scenario reduction is to approximate a given discrete distributionwith another discrete distribution that has fewer atoms. We distinguishcontinuous scenario reduction, where the new atoms may be chosen freely, ...
Quantitative Stability Analysis for Minimax Distributionally Robust RiskOptimization
This paper considers distributionally robust formulations of a two stage stochastic programmingproblem with the objective of minimizing a distortion risk of the minimal cost incurred at the secondstage.We carry out stability ...
Optimal scenario generation and reduction in stochastic programming
Scenarios are indispensable ingredients for the numerical solution of stochastic optimization problems. Earlier approaches for optimal scenario generation and reduction are based on stability arguments involving distances ...