An extension of the DQA algorithm to convex stochastic programs

Berger, A., Mulvey, J., & Ruszczynski, A. (1994). An extension of the DQA algorithm to convex stochastic programs. SIAM Journal on Optimization 4 (4) 735-753. 10.1137/0804043.

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The diagonal quadratic approximation (DQA) algorithm is extended for the case of risk-averse utility and other nonlinear functions associated with stochastic programs. The method breaks the stochastic program into a sequence of smaller quadratic programming subproblems that can be executed in parallel. Each subproblem is solved approximately by means of a convex version of a primal-dual interior-point code (LOQO). Convergence of the distributed DQA method is discussed.

All communication takes place among neighboring processors rather than via a master routine leading to an efficient distributed implementation. Results with a realworld airline planning model possessing a convex objective, 155,320 linear constraints and 303,600 variables, show the DQA algorithm’s efficiency. The interior point direct solver (convex-LOQO) is shown to solve moderate-size stochastic programs in a small number of iterations (under 50).

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Item Type: Article
Research Programs: Optimization under Uncertainty (OPT)
Bibliographic Reference: SIAM Journal on Optimization; 4:735-753 [1994]
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:03
Last Modified: 27 Aug 2021 17:35

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