Back to Search
Start Over
A DECOMPOSITION ALGORITHM FOR TWO-STAGE STOCHASTIC PROGRAMS WITH NONCONVEX RECOURSE FUNCTIONS.
- Source :
-
SIAM Journal on Optimization . 2024, Vol. 34 Issue 1, p306-335. 30p. - Publication Year :
- 2024
-
Abstract
- In this paper, we have studied a decomposition method for solving a class of non-convex two-stage stochastic programs, where both the objective and constraints of the second-stage problem are nonlinearly parameterized by the first-stage variables. Due to the failure of the Clarke regularity of the resulting nonconvex recourse function, classical decomposition approaches such as Benders decomposition and (augmented) Lagrangian-based algorithms cannot be directly generalized to solve such models. By exploring an implicitly convex-concave structure of the recourse function, we introduce a novel decomposition framework based on the so-called partial Moreau envelope. The algorithm successively generates strongly convex quadratic approximations of the recourse function based on the solutions of the second-stage convex subproblems and adds them to the first-stage mas-ter problem. Convergence has been established for both a fixed number of scenarios and a sequential internal sampling strategy. Numerical experiments are conducted to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Subjects :
- *DECOMPOSITION method
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 10526234
- Volume :
- 34
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- SIAM Journal on Optimization
- Publication Type :
- Academic Journal
- Accession number :
- 176824644
- Full Text :
- https://doi.org/10.1137/22M1488533