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We introduce Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive… Expand This paper evaluates the real-time price-based demand response (DR) management for residential appliances via stochastic… Expand We present a new family of subgradient methods that dynamically incorporate knowledge of the geometry of the data observed in… Expand We present a new approach to motion planning using a stochastic trajectory optimization framework. The approach relies on… Expand In this paper we consider optimization problems where the objective function is given in a form of the expectation. A basic… Expand This comprehensive book offers 504 main pages divided into 17 chapters. In addition, five very useful and clearly written… Expand This paper introduces a method for solving numerical dynamic stochastic optimization problems that avoids rootfinding operations… Expand A unique interdisciplinary foundation for real-world problem solvingStochastic search and optimization techniques are used in a… Expand From the Publisher:
* Unique in its survey of the range of topics.
* Contains a strong, interdisciplinary format that will… Expand This paper presents a methodology for the solution of multistage stochastic optimization problems, based on the approximation of… Expand