A path-following algorithm for stochastic quadratically constrained convex quadratic programming in a Hilbert space

Document Type : Original paper

Authors

Department of Mathematics, The University of Jordan, Amman 11942, Jordan

Abstract

We propose logarithmic-barrier decomposition-based interior-point algorithms for solving two-stage stochastic quadratically constrained convex quadratic programming problems in a Hilbert space. We prove the polynomial complexity of the proposed algorithms, and show that this complexity is independent on the choice of the Hilbert space, and hence it coincides with the best-known complexity estimates in the finite-dimensional case. We also apply our results on a concrete example from the stochastic control theory.

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References

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Communications in Combinatorics and Optimization
Volume 9, Issue 2
June 2024
Pages 353-387

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