Corpus ID: 220347369

A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating

@article{Sabanis2020AFD,
  title={A fully data-driven approach to minimizing CVaR for portfolio of assets via SGLD with discontinuous updating},
  author={S. Sabanis and Y. Zhang},
  journal={arXiv: Portfolio Management},
  year={2020}
}
  • S. Sabanis, Y. Zhang
  • Published 2020
  • Computer Science, Economics, Mathematics
  • arXiv: Portfolio Management
A new approach in stochastic optimization via the use of stochastic gradient Langevin dynamics (SGLD) algorithms, which is a variant of stochastic gradient decent (SGD) methods, allows us to efficiently approximate global minimizers of possibly complicated, high-dimensional landscapes. With this in mind, we extend here the non-asymptotic analysis of SGLD to the case of discontinuous stochastic gradients. We are thus able to provide theoretical guarantees for the algorithm's convergence in… Expand

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