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In E-lab exercises, group members are encouraged to put forward ideas about a problem and how it may be solved, to generate as many (including unusable) ideas as possible.
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Thereafter, two multi-objective case studies are solved to generate the complete Pareto set.
These problems, which correspond to mixed-integer linear programs, are solved to generate approximate solutions with improved objective value.
Instead a linear elliptic system and an inversion problem is solved to generate the grid in the interior of the surface.
If instead the moments are known, the extremal distributions can be found and the roots of their generating functions can be solved to find the bounds on extinction.
Instances of this linear programming problem can be solved iteratively to generate a solution to the finite horizon routing problem.
A big reason that rational-expectations models proved so alluring to economists was their tractability: with some clever math and a computer, they could be "solved out" to generate explicit solutions for important economic variables, such as unemployment and inflation.
In this stage, we develop a parametric distance model that can be solved repeatedly to generate the various sets of weights for the discrete values of the parameter (theta ), such that (0le theta le theta _{max } ).
The above model was solved numerically to generate a nomograph that relates dimensionless substrate concentration at biofilm liquid interface, dimensionless substrate flux, dimensionless biofilm thickness and dimensionless minimum substrate concentration required to sustain the flux (Kim and Suidan 1989).
The sensitivity equations to be solved are generated via automatic differentiation.
This problem may be solved by generating additional lines carrying different insertion sites to select those expressing Gla80 in all cells.
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