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- W2247451106 abstract "In this paper, we consider a bilevel polynomial optimization problem where the objective and the constraint functions of both the upper-and the lower-level problems are polynomials. We present methods for finding its global minimizers and global minimum using a sequence of semidefinite programming (SDP) relaxations and provide convergence results for the methods. Our scheme for problems with a convex lower-level problem involves solving a transformed equivalent single-level problem by a sequence of SDP relaxations, whereas our approach for general problems involving a nonconvex polynomial lower-level problem solves a sequence of approximation problems via another sequence of SDP relaxations." @default.
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- W2247451106 date "2016-01-01" @default.
- W2247451106 modified "2023-10-14" @default.
- W2247451106 title "Convergent Semidefinite Programming Relaxations for Global Bilevel Polynomial Optimization Problems" @default.
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- W2247451106 doi "https://doi.org/10.1137/15m1017922" @default.
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