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- W122117064 abstract "In nonlinear optimization, the dual problem is generally not easier to solve than the primal problem. Convex separable optimization problems, frequently arising in electrical and mechanical engineering, constitute a notable exception to the rule. The dual problem is to optimize the dual objective function l over a non-negative orthant, and the evaluation of l reduces to the execution of independent linear searches only. To generalize the idea, we consider partially-separable problems with objective and constraint functions such that the Hessians are block-diagonal matrices with at most 2*2 blocks. The evaluation of the dual objective function is accordingly reduced to a number of independent planar searches. Obviously, 3*3 blocks would lead to spatial searches, etc. Finally, we consider the potential of the dual approach for execution on parallel computers." @default.
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- W122117064 date "1989-01-01" @default.
- W122117064 modified "2023-09-25" @default.
- W122117064 title "Dual Methods for Large-scale, Partially-separable Nonlinear Optimization" @default.
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- W122117064 doi "https://doi.org/10.1007/978-3-642-83707-4_29" @default.
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