Gradient-Based Algorithms for Zeroth-Order Optimization
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This monograph deals with methods for stochastic or data-driven optimization. The overall goal of these methods is to minimize a certain parameter-dependent objective function that for any parameter value is an expectation of a noisy sample performance objective whose measurement can be made from a real system or a simulation device.
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