A decomposition-based warm-start method for stochastic programming

Marco Colombo; Andreas Grothey

doi:10.1007/s10589-012-9530-7

A decomposition-based warm-start method for stochastic programming

Research output: Contribution to journal › Article › peer-review

Abstract

In this paper we propose a warm-start technique for interior point methods applicable to multi-stage stochastic linear programming problems. The main idea is to generate an initial point by decomposing the problem at the second stage and using an approximate solution of the subproblems as a starting point for the complete instance. We analyse this scheme and produce theoretical conditions under which the warm-start iterate is successful. We describe the implementation within the OOPS solver and the results of the numerical tests we performed.

Original language	English
Pages (from-to)	311-340
Number of pages	30
Journal	Computational optimization and applications
Volume	55
Issue number	2
DOIs	https://doi.org/10.1007/s10589-012-9530-7
Publication status	Published - Jun 2013

Keywords / Materials (for Non-textual outputs)

Stochastic programming, Interior point methods, Warm-starting, Structure exploitation

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10.1007/s10589-012-9530-7

A decomposition-based warm-start method for stochastic programmingSubmitted manuscript, 798 KB

Warmstarting techniques for stochastic programming problems solved by interior point methods.
Grothey, A. (Principal Investigator)
EPSRC
1/04/07 → 31/03/09
Project: Research

Cite this

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AB - In this paper we propose a warm-start technique for interior point methods applicable to multi-stage stochastic linear programming problems. The main idea is to generate an initial point by decomposing the problem at the second stage and using an approximate solution of the subproblems as a starting point for the complete instance. We analyse this scheme and produce theoretical conditions under which the warm-start iterate is successful. We describe the implementation within the OOPS solver and the results of the numerical tests we performed.

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A decomposition-based warm-start method for stochastic programming

Abstract

Keywords / Materials (for Non-textual outputs)

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Warmstarting techniques for stochastic programming problems solved by interior point methods.

Cite this