Abstract
For a stochastic programming problem with a nonsmooth objective a recursive stochastic subgradient method is proposed in which successive directions are obtained from quadratic programming subproblems. The subproblems result from linearization of original constrains and from approximation of the objective by a quadratic function involving stochastic ϵ-subgradient estimates constructed in the course of computation. A special Lyapunov function technique is used to show that the method is convergent with probability 1 to stationary points and recovers the subgradient and Lagrange multipliers that appear in necessary conditions of optimality.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
