With the growing use of computer modeling and simulation, in all aspects of engineering, the scope of traditional optimization has to be extended to include simulation models. Some unique aspects have to be addressed while optimizing via stochastic simulation models. The optimization procedure has to explicitly account for the randomness inherent in the stochastic measures predicted by the mode...

Sparse signal recovery from a small number of random measurements is a well known NP-hard to solve combinatorial optimization problem, with important applications in signal and image processing. The standard approach to the sparse signal recovery problem is based on the basis pursuit method. This approach requires the solution of a large convex optimization problem, and therefore suffers from h...

Abstract A common approach in aerodynamic design is to optimize a performance function—provided some constraints—defined by choice of an model at nominal operating conditions. Practical experience indicates that such deterministic may result considerably sub-optimal designs when the adopted does not lead accurate predictions, or actual conditions differ from those considered design. One address...

abstract: enviromental concerns, improvements in renewable energy technologies, governmental incentives for the use of these resources, and increased t&d; costs, are the main factors driving the energy sector into a new era, where considerable portions of electrical demand will be met through widespread installation of distributed energy resources (ders). the virtual power plant (vpp) is a dece...

We present a stochastic descent algorithm for unconstrained optimization that is particularly efficient when the objective function slow to evaluate and gradients are not easily obtained, as in some PDE-constrained machine learning problems. The maps gradient onto low-dimensional random subspace of dimension $$\ell$$ at each iteration, similar coordinate but without restricting directional der...

We propose a new family of job scheduling policies for parallel computer systems that can be optimized to adapt to changes in the workload. Simulation optimization is used to reveal important properties of optimal job scheduling policies. For this optimization a new approach is suggested that combines two recent stochastic optimization methods: the nested partitions method and ordinal optimizat...

Problem statement: The most important character within optimization problem is the uncertainty of the future returns. Approach: To handle such problems, we utilized probabilistic methods alongside with optimization techniques. We developed single stage and two stage stochastic programming with recourse. The models were developed for risk adverse investors and the objective of the stochastic pro...