Penalty methods with stochastic approximation for stochastic nonlinear programming

In this paper, we propose a class of penalty methods with stochastic approximation for solving stochastic nonlinear programming problems. We assume that only noisy gradients or function values of the objective function are available via calls to a stochastic first-order or zeroth-order oracle. In each iteration of the proposed methods, we minimize an exact penalty function which is nonsmooth an...

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Geometric Programming with Stochastic Parameter

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Stochastic Approximation Approach to Stochastic Programming

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Robust Stochastic Approximation Approach to Stochastic Programming

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Penalty methods with stochastic approximation for stochastic nonlinear programming

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