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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 ...