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(S2) Dual stochastic subgradient.
Furthermore, similar stochastic subgradient iteration algorithm as Algorithm 1 can also be proposed.
The core of the dual stochastic subgradient descent algorithm is the dual iteration (S3).
These observations motivate the introduction of the following dual stochastic subgradient descent algorithm.
Formally, (84) implies that s(h,Λ) is a stochastic subgradient of the dual function.
The convergence of the stochastic subgradient iteration can be guaranteed by the following lemma.
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As a consequence, using stochastic subgradients as descent directions results in an algorithm that is computationally lighter.
The stochastic quasigradient method only needs this current state information of the system and utilizes it to form the stochastic subgradients of at iteration.
The purpose of the primal iteration (S1) is to compute the stochastic subgradients in (S2) that are needed to implement the dual descent update in (S3).
Let Ŝ 2 ≥ E s h, Λ 2 be a bound on the second moment of the norm of the stochastic subgradients s(h,Λ) and assume the same hypotheses of Theorem 1. Sequences p ( N ) and x ( N ) are such that: (i) Feasibility.
Some consider subgradient learning method while transforming chance constraints into stochastic programming problems [21 23].
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