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A long-term optimisation model decomposes into thousands of hourly models, which can be formulated as linear programming (LP) problems.
Our stability conditions have the property that when specialized to linear impulsive systems, the stability tests can be formulated as Linear Matrix Inequalities (LMIs).
Lawler et al. [16] pointed out that the problem of scheduling identical jobs on uniform parallel machines could be formulated as linear assignment problem and solved accordingly in polynomial time.
By approximating polytopic operating regions by ellipsoids and using the singular value decomposition technique to treat the constraint of matrix equality, the suggested control method can be formulated as linear matrix inequalities (LMIs), and solved much more efficiently than existing methods which could be only cast as bilinear matrix inequalities.
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The design conditions are formulated as Linear Matrix Inequalities (LMIs).
Linear optimization Based Techniques [22, 23] Relay coordination problem is formulated as linear programming problem.
In the literature, bounds on WLPs are formulated as linear programming (LP) problems.
The suggested control method is formulated as linear matrix inequalities (LMIs).
The allocations of sources to demands are formulated as linear programming models.
These conditions are formulated as Linear Matrix Inequalities (LMIs) which are easily tractable using standards optimization algorithms.
The model is formulated as linear Mixed Integer Programming (MIP) and solved to optimality using standard branch-and-bound techniques.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com