A manifold-based approach to sparse global constraint satisfaction problems

Ali Baharev (), Arnold Neumaier and Hermann Schichl
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Ali Baharev: University of Vienna
Arnold Neumaier: University of Vienna
Hermann Schichl: University of Vienna

Journal of Global Optimization, 2019, vol. 75, issue 4, No 3, 949-971

Abstract: Abstract We consider square, sparse nonlinear systems of equations whose Jacobian is structurally nonsingular, with reasonable bound constraints on all variables. We propose an algorithm for finding good approximations to all well-separated solutions of such systems. We assume that the input system is ordered such that its Jacobian is in bordered block lower triangular form with small diagonal blocks and with small border width; this can be performed fully automatically with off-the-shelf decomposition methods. Five decades of numerical experience show that models of technical systems tend to decompose favorably in practice. Once the block decomposition is available, we reduce the task of solving the large nonlinear system of equations to that of solving a sequence of low-dimensional ones. The most serious weakness of this approach is well-known: It may suffer from severe numerical instability. The proposed method resolves this issue with the novel backsolve step. We study the effect of the decomposition on a sequence of challenging problems. Beyond a certain problem size, the computational effort of multistart (no decomposition) grows exponentially. In contrast, thanks to the decomposition, for the proposed method the computational effort grows only linearly with the problem size. It depends on the problem size and on the hyperparameter settings whether the decomposition and the more sophisticated algorithm pay off. Although there is no theoretical guarantee that all solutions will be found in the general case, increasing the so-called sample size hyperparameter improves the robustness of the proposed method.

Keywords: Decomposition methods; Diakoptics; Large-scale systems of equations; Numerical instability; Sparse matrices; Tearing (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s10898-019-00805-x

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