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The NLP formulation is solved by the IPOPT and the SOCP formulation is solved by the Gurobi.
The MILP formulation is solved optimally for medium-sized instances.
The proposed set partitioning-based formulation is solved using a branch-and-price approach.
This possibilistic probabilistic formulation is solved in an uncertain power flow (UPF) study problem.
The nonlinear least squares optimization formulation is solved by (1) programming in Mathematica and (2) by applying genetic algorithm.
The set of velocity-pressure coupled governing equations resulted by the implicit formulation is solved using Choin's projection method.
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This formulation was solved using an optimization library for a small data set.
Bruhn et al. (2005) merged the benefits of all of these previous approaches in a combined local - to - global approach, where a robust Horn and Schunck formulation was solved at different spatial scales, effectively incorporating the benefits of the approach by Lucas and Kanade.
The global system obtained after discretization of the finite element (FE) formulations is solved via a Krylov subspace iterative method (GMRES).
The mathematical formulations were solved implicitly for pressure, water saturation, and gas saturation.
The proposed concave formulations are solved via a Quasi-Newton update and Armijo line search.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com